Redesign of a cross-reactive antibody to dengue virus with broad-spectrum activity and increased in vivo potency

Development and experimental validation of computational methods for human antibody affinity enhancement

High affinity is crucial for the efficacy and specificity of antibody. Due to involving high-throughput screens, biological experiments for antibody affinity maturation are time-consuming and have a low success rate. Precise computational-assisted antibody design promises to accelerate this process, but there is still a lack of effective computational methods capable of pinpointing beneficial mutations within the complementarity-determining region (CDR) of antibodies. Moreover, random mutations often lead to challenges in antibody expression and immunogenicity. In this study, to enhance the affinity of a human antibody against avian influenza virus, a CDR library was constructed and evolutionary information was acquired through sequence alignment to restrict the mutation positions and types. Concurrently, a statistical potential methodology was developed based on amino acid interactions between antibodies and antigens to calculate potential affinity-enhanced antibodies, which were further subjected to molecular dynamics simulations. Subsequently, experimental validation confirmed that a point mutation enhancing 2.5-fold affinity was obtained from 10 designs, resulting in the antibody affinity of 2 nM. A predictive model for antibody-antigen interactions based on the binding interface was also developed, achieving an Area Under the Curve (AUC) of 0.83 and a precision of 0.89 on the test set. Lastly, a novel approach involving combinations of affinity-enhancing mutations and an iterative mutation optimization scheme similar to the Monte Carlo method were proposed. This study presents computational methods that rapidly and accurately enhance antibody affinity, addressing issues related to antibody expression and immunogenicity.

An Engineered N-Glycosylated Dengue Envelope Protein Domain III Facilitates Epitope-Directed Selection of Potently Neutralizing and Minimally Enhancing Antibodies

The envelope protein of dengue virus (DENV) is a primary target of the humoral immune response. The domain III of the DENV envelope protein (EDIII) is known to be the target of multiple potently neutralizing antibodies. One such antibody is 3H5, a mouse antibody that binds strongly to EDIII and potently neutralizes DENV serotype 2 (DENV-2) with unusually minimal antibody-dependent enhancement (ADE). To selectively display the binding epitope of 3H5, we strategically modified DENV-2 EDIII by shielding other known epitopes with engineered N-glycosylation sites. The modifications resulted in a glycosylated EDIII antigen termed "EDIII mutant N". This antigen was successfully used to sift through a dengue-immune scFv-phage library to select for scFv antibodies that bind to or closely surround the 3H5 epitope. The selected scFv antibodies were expressed as full-length human antibodies and showed potent neutralization activity to DENV-2 with low or negligible ADE resembling 3H5. These findings not only demonstrate the capability of the N-glycosylated EDIII mutant N as a tool to drive an epitope-directed antibody selection campaign but also highlight its potential as a dengue immunogen. This glycosylated antigen shows promise in focusing the antibody response toward a potently neutralizing epitope while reducing the risk of antibody-dependent enhancement.

Recombinant human scFv antibody fragments against phospholipase A2 from Naja naja and Echis carinatus snake venoms: In vivo neutralization and mechanistic insights

Snake envenomation results in a range of clinical sequelae, and widely used animal-based conventional antivenoms exhibit several limitations including the adverse immunological effects in human snake bite victims. Therefore, human monoclonal anti-snake venom antibodies or fragments can be an alternate therapy for overcoming the existing limitations. We developed venom-neutralizing humanized scFv antibodies and analyzed biochemical mechanisms associated with the inhibition of toxicity. Tomlinson I and J human scFv antibody libraries were screened against Naja naja and Echis carinatus venoms, and seven unique scFv antibodies were obtained. Further, specific toxins of snake venom interacting with each of these scFvs were identified, and phospholipase A2 (PLA2) was found to be prominently captured by the phage-anchored scFv antibodies. Our study indicated PLA2 to be one of the abundant toxins in Naja naja and Echis carinatus venom samples. The scFvs binding to PLA2 were used to perform in vivo survival assay using the mouse model and in vitro toxin inhibition assays. scFv N194, which binds to acidic PLA2, protected 50% of mice treated with Naja naja venom. Significant prolongation of survival time and 16% survival were observed in Echis carinatus venom-challenged mice treated with scFv E113 and scFv E10, respectively. However, a combination comprised of an equal amount of two scFvs, E113 and E10, both interacting with basic PLA2, exhibited synergistically enhanced survival of 33% in Echis carinatus venom-challenged mice. No such synergistically enhanced survival was observed in the case of combinatorial treatment with anti-Naja naja scFvs, N194, and N248. These scFvs demonstrated partial inhibition of venom-induced myotoxicity, and E113 also inhibited hemolysis by 50%, which corroborates the enhanced survival during combinatorial treatment in Echis carinatus venom-challenged mice.

Enhancing antibody affinity through experimental sampling of non-deleterious CDR mutations predicted by machine learning

The application of machine learning (ML) models to optimize antibody affinity to an antigen is gaining prominence. Unfortunately, the small and biased nature of the publicly available antibody-antigen interaction datasets makes it challenging to build an ML model that can accurately predict binding affinity changes due to mutations (ΔΔG). Recognizing these inherent limitations, we reformulated the problem to ask whether an ML model capable of classifying deleterious vs non-deleterious mutations can guide antibody affinity maturation in a practical setting. To test this hypothesis, we developed a Random Forest classifier (Antibody Random Forest Classifier or AbRFC) with expert-guided features and integrated it into a computational-experimental workflow. AbRFC effectively predicted non-deleterious mutations on an in-house validation dataset that is free of biases seen in the publicly available training datasets. Furthermore, experimental screening of a limited number of predictions from the model (<10^2 designs) identified affinity-enhancing mutations in two unrelated SARS-CoV-2 antibodies, resulting in constructs with up to 1000-fold increased binding to the SARS-COV-2 RBD. Our findings indicate that accurate prediction and screening of non-deleterious mutations using machine learning offers a powerful approach to improving antibody affinity.

Development of Therapeutic Monoclonal Antibodies for Emerging Arbovirus Infections

Antibody-based passive immunotherapy has been used effectively in the treatment and prophylaxis of infectious diseases. Outbreaks of emerging viral infections from arthropod-borne viruses (arboviruses) represent a global public health problem due to their rapid spread, urging measures and the treatment of infected individuals to combat them. Preparedness in advances in developing antivirals and relevant epidemiological studies protect us from damage and losses. Immunotherapy based on monoclonal antibodies (mAbs) has been shown to be very specific in combating infectious diseases and various other illnesses. Recent advances in mAb discovery techniques have allowed the development and approval of a wide number of therapeutic mAbs. This review focuses on the technological approaches available to select neutralizing mAbs for emerging arbovirus infections and the next-generation strategies to obtain highly effective and potent mAbs. The characteristics of mAbs developed as prophylactic and therapeutic antiviral agents for dengue, Zika, chikungunya, West Nile and tick-borne encephalitis virus are presented, as well as the protective effect demonstrated in animal model studies.

Improved HIV-1 neutralization breadth and potency of V2-apex antibodies by in silico design

Broadly neutralizing antibodies (bNAbs) against HIV can reduce viral transmission in humans, but an effective therapeutic will require unusually high breadth and potency of neutralization. We employ the OSPREY computational protein design software to engineer variants of two apex-directed bNAbs, PGT145 and PG9RSH, resulting in increases in potency of over 100-fold against some viruses. The top designed variants improve neutralization breadth from 39% to 54% at clinically relevant concentrations (IC80 < 1 μg/mL) and improve median potency (IC80) by up to 4-fold over a cross-clade panel of 208 strains. To investigate the mechanisms of improvement, we determine cryoelectron microscopy structures of each variant in complex with the HIV envelope trimer. Surprisingly, we find the largest increases in breadth to be a result of optimizing side-chain interactions with highly variable epitope residues. These results provide insight into mechanisms of neutralization breadth and inform strategies for antibody design and improvement.

Dengue virus neutralizing antibody: a review of targets, cross-reactivity, and antibody-dependent enhancement

Dengue is the most common viral infection spread by mosquitoes, prevalent in tropical countries. The acute dengue virus (DENV) infection is a benign and primarily febrile illness. However, secondary infection with alternative serotypes can worsen the condition, leading to severe and potentially fatal dengue. The antibody raised by the vaccine or the primary infections are frequently cross-reactive; however, weakly neutralizing, and during subsequent infection, they may increase the odds of antibody-dependent enhancement (ADE). Despite that, many neutralizing antibodies have been identified against the DENV, which are thought to be useful in reducing dengue severity. Indeed, an antibody must be free from ADE for therapeutic application, as it is pretty common in dengue infection and escalates disease severity. Therefore, this review has described the critical characteristics of DENV and the potential immune targets in general. The primary emphasis is given to the envelope protein of DENV, where potential epitopes targeted for generating serotype-specific and cross-reactive antibodies have critically been described. In addition, a novel class of highly neutralizing antibodies targeted to the quaternary structure, similar to viral particles, has also been described. Lastly, we have discussed different aspects of the pathogenesis and ADE, which would provide significant insights into developing safe and effective antibody therapeutics and equivalent protein subunit vaccines.

The validation of artificial anti-monkeypox antibodies by in silico and experimental approaches

As a result of smallpox immunization programs that ended more than 40 years ago, a significant portion of the world's population is not immune. Moreover, due to the lack of anti-monkeypox drugs and vaccines against monkeypox, the spread of this virus may be the beginning of another challenge. In this study, novel antibodies against monkeypox virus were modeled based on a heavy chain of human antibody and a small peptide fragment. Docking of modeled antibodies with C19L protein showed the range of docking energy, and root-mean-square deviation (RMSD) was from -124 to -154 kcal/mL and 4-6 angstrom, respectively. Also, docking of modeled antibodies-C19L complex with gamma Fc receptor type I illustrated the range of docking energy, and RMSD was from -132 to -155 kcal/ml and 5-7 angstrom, respectively. Moreover, molecular dynamics simulation showed that antibody 62 had the highest stability with the lowest energy level and RMSD. Interestingly, no modeled antibodies had immunogenicity, allergenicity, and toxicity. Although all of them had good stability, only antibodies 25, 28, 54, and 62 had a half-life of >10 h. Moreover, the interaction between C19L protein and anti-C19L antibodies (wild-type and synthetic) was evaluated by the SPR method. We found that KD in synthetic antibodies was lower than wild antibody. In terms of δH°, TδS°, and δG°, the results were consistent with binding parameters. Here, the lowest value of thermodynamic parameters was obtained for antibody 62. These data show that the synthetic antibodies, especially antibody 62, had a higher affinity than the wild-type antibody.

Learned features of antibody-antigen binding affinity

Defining predictors of antigen-binding affinity of antibodies is valuable for engineering therapeutic antibodies with high binding affinity to their targets. However, this task is challenging owing to the huge diversity in the conformations of the complementarity determining regions of antibodies and the mode of engagement between antibody and antigen. In this study, we used the structural antibody database (SAbDab) to identify features that can discriminate high- and low-binding affinity across a 5-log scale. First, we abstracted features based on previously learned representations of protein-protein interactions to derive 'complex' feature sets, which include energetic, statistical, network-based, and machine-learned features. Second, we contrasted these complex feature sets with additional 'simple' feature sets based on counts of contacts between antibody and antigen. By investigating the predictive potential of 700 features contained in the eight complex and simple feature sets, we observed that simple feature sets perform comparably to complex feature sets in classification of binding affinity. Moreover, combining features from all eight feature-sets provided the best classification performance (median cross-validation AUROC and F1-score of 0.72). Of note, classification performance is substantially improved when several sources of data leakage (e.g., homologous antibodies) are not removed from the dataset, emphasizing a potential pitfall in this task. We additionally observe a classification performance plateau across diverse featurization approaches, highlighting the need for additional affinity-labeled antibody-antigen structural data. The findings from our present study set the stage for future studies aimed at multiple-log enhancement of antibody affinity through feature-guided engineering.

Optimizing Antibody Affinity and Developability Using a Framework-CDR Shuffling Approach-Application to an Anti-SARS-CoV-2 Antibody

The computational methods used for engineering antibodies for clinical development have undergone a transformation from three-dimensional structure-guided approaches to artificial-intelligence- and machine-learning-based approaches that leverage the large sequence data space of hundreds of millions of antibodies generated by next-generation sequencing (NGS) studies. Building on the wealth of available sequence data, we implemented a computational shuffling approach to antibody components, using the complementarity-determining region (CDR) and the framework region (FWR) to optimize an antibody for improved affinity and developability. This approach uses a set of rules to suitably combine the CDRs and FWRs derived from naturally occurring antibody sequences to engineer an antibody with high affinity and specificity. To illustrate this approach, we selected a representative SARS-CoV-2-neutralizing antibody, H4, which was identified and isolated previously based on the predominant germlines that were employed in a human host to target the SARS-CoV-2-human ACE2 receptor interaction. Compared to screening vast CDR libraries for affinity enhancements, our approach identified fewer than 100 antibody framework-CDR combinations, from which we screened and selected an antibody (CB79) that showed a reduced dissociation rate and improved affinity against the SARS-CoV-2 spike protein (7-fold) when compared to H4. The improved affinity also translated into improved neutralization (>75-fold improvement) of SARS-CoV-2. Our rapid and robust approach for optimizing antibodies from parts without the need for tedious structure-guided CDR optimization will have broad utility for biotechnological applications.

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.

Machine Learning Detects Anti-DENV Signatures in Antibody Repertoire Sequences

Dengue infection is a global threat. As of today, there is no universal dengue fever treatment or vaccines unreservedly recommended by the World Health Organization. The investigation of the specific immune response to dengue virus would support antibody discovery as therapeutics for passive immunization and vaccine design. High-throughput sequencing enables the identification of the multitude of antibodies elicited in response to dengue infection at the sequence level. Artificial intelligence can mine the complex data generated and has the potential to uncover patterns in entire antibody repertoires and detect signatures distinctive of single virus-binding antibodies. However, these machine learning have not been harnessed to determine the immune response to dengue virus. In order to enable the application of machine learning, we have benchmarked existing methods for encoding biological and chemical knowledge as inputs and have investigated novel encoding techniques. We have applied different machine learning methods such as neural networks, random forests, and support vector machines and have investigated the parameter space to determine best performing algorithms for the detection and prediction of antibody patterns at the repertoire and antibody sequence levels in dengue-infected individuals. Our results show that immune response signatures to dengue are detectable both at the antibody repertoire and at the antibody sequence levels. By combining machine learning with phylogenies and network analysis, we generated novel sequences that present dengue-binding specific signatures. These results might aid further antibody discovery and support vaccine design.

Prospective Artificial Intelligence to Dissect the Dengue Immune Response and Discover Therapeutics

Dengue virus (DENV) poses a serious threat to global health as the causative agent of dengue fever. The virus is endemic in more than 128 countries resulting in approximately 390 million infection cases each year. Currently, there is no approved therapeutic for treatment nor a fully efficacious vaccine. The development of therapeutics is confounded and hampered by the complexity of the immune response to DENV, in particular to sequential infection with different DENV serotypes (DENV1-5). Researchers have shown that the DENV envelope (E) antigen is primarily responsible for the interaction and subsequent invasion of host cells for all serotypes and can elicit neutralizing antibodies in humans. The advent of high-throughput sequencing and the rapid advancements in computational analysis of complex data, has provided tools for the deconvolution of the DENV immune response. Several types of complex statistical analyses, machine learning models and complex visualizations can be applied to begin answering questions about the B- and T-cell immune responses to multiple infections, antibody-dependent enhancement, identification of novel therapeutics and advance vaccine research.

An antibody engineering platform using amino acid networks: A case study in development of antiviral therapeutics

We present here a case study of an antibody-engineering platform that selects, modifies, and assembles antibody parts to construct novel antibodies. A salient feature of this platform includes the role of amino acid networks in optimizing framework regions (FRs) and complementarity determining regions (CDRs) to engineer new antibodies with desired structure-function relationships. The details of this approach are described in the context of its utility in engineering ZAb_FLEP, a potent anti-Zika virus antibody. ZAb_FLEP comprises of distinct parts, including heavy chain and light chain FRs and CDRs, with engineered features such as loop lengths and optimal epitope-paratope contacts. We demonstrate, with different test antibodies derived from different FR-CDR combinations, that despite these test antibodies sharing high overall sequence similarity, they yield diverse functional readouts. Furthermore, we show that strategies relying on one dimensional sequence similarity-based analyses of antibodies miss important structural nuances of the FR-CDR relationship, which is effectively addressed by the amino acid networks approach of this platform.

Residue-based pharmacophore approaches to study protein-protein interactions

This review focuses on pharmacophore approaches in researching protein interfaces that bind protein ligands. Pharmacophore descriptions of binding interfaces that employ molecular dynamics simulation can account for effects of solvation and conformational flexibility. In addition, these calculations provide an approximation to entropic considerations and as such, a better approximation of the free energy of binding. Residue-based pharmacophore approaches can facilitate a variety of drug discovery tasks such as the identification of receptor-ligand partners, identifying their binding poses, designing protein interfaces for selectivity, or defining a reduced mutational combinatorial exploration for subsequent experimental engineering techniques by orders of magnitudes.

A complex between the Zika virion and the Fab of a broadly cross-reactive neutralizing monoclonal antibody revealed by cryo-EM and single particle analysis at 4.1 Å resolution

Zika virus (ZIKV) recently emerged as a major public health concern because it can cause fetal microcephaly and neurological disease such as the Guillain-Barré syndrome. A particularly potent class of broadly neutralizing antibodies (nAbs) targets a quaternary epitope located at the interface of two envelope proteins monomers, exposed at the surface of the mature virion. This “E-dimer-dependent epitope” (EDE), comprises the fusion loop of one monomer at the tip of domain II of E and a portion of the domains I and III of the adjacent monomer. Since this epitope largely overlaps with the binding site of the precursor membrane protein (prM) during Zika virion maturation, its molecular surface is evolutionary conserved in flaviviruses such as Dengue and Zika viruses, and can elicit antibodies that broadly neutralize various ZIKV strains. Here, we present a cryo-EM reconstruction at 4.1 Å resolution of the virion bound to the antigen binding fragment (Fab) of an antibody that targets this mutationally-constrained quaternary epitope. The Fab incompletely covers the surface of the virion as it does not bind next to its 5-fold icosahedral axes. The structure reveals details of the binding mode of this potent neutralizing class of antibodies and can inform the design of immunogens and vaccines targeting this conserved epitope.

Adaptive Immunity to Dengue Virus: Slippery Slope or Solid Ground for Rational Vaccine Design?

The four serotypes of dengue virus are the most widespread causes of arboviral disease, currently placing half of the human population at risk of infection. Pre-existing immunity to one dengue virus serotype can predispose to severe disease following secondary infection with a different serotype. The phenomenon of immune enhancement has complicated vaccine development and likely explains the poor long-term safety profile of a recently licenced dengue vaccine. Therefore, alternative vaccine strategies should be considered. This review summarises studies dissecting the adaptive immune responses to dengue virus infection and (experimental) vaccination. In particular, we discuss the roles of (i) neutralising antibodies, (ii) antibodies to non-structural protein 1, and (iii) T cells in protection and pathogenesis. We also address how these findings could translate into next-generation vaccine approaches that mitigate the risk of enhanced dengue disease. Finally, we argue that the development of a safe and efficacious dengue vaccine is an attainable goal.

Computer-guided library generation applied to the optimization of single-domain antibodies

Computer-guided library generation is a plausible strategy to optimize antibodies. Herein, we report the improvement of the affinity of a single-domain camelid antibody for its antigen using such approach. We first conducted experimental and computational alanine scanning to describe the precise energetic profile of the antibody-antigen interaction surface. Based on this characterization, we hypothesized that in-silico mutagenesis could be employed to guide the development of a small library for phage display with the goal of improving the affinity of an antibody for its antigen. Optimized antibody mutants were identified after three rounds of selection, in which an alanine residue at the core of the antibody-antigen interface was substituted by residues with large side-chains, generating diverse kinetic responses, and resulting in greater affinity (>10-fold) for the antigen.

Broadly neutralizing human antibodies against dengue virus identified by single B cell transcriptomics

Eliciting broadly neutralizing antibodies (bNAbs) against the four dengue virus serotypes (DENV1-4) that are spreading into new territories is an important goal of vaccine design. To define bNAb targets, we characterized 28 antibodies belonging to expanded and hypermutated clonal families identified by transcriptomic analysis of single plasmablasts from DENV-infected individuals. Among these, we identified J9 and J8, two somatically related bNAbs that potently neutralized DENV1-4. Mutagenesis studies showed that the major recognition determinants of these bNAbs are in E protein domain I, distinct from the only known class of human bNAbs against DENV with a well-defined epitope. B cell repertoire analysis from acute-phase peripheral blood suggested that J9 and J8 followed divergent somatic hypermutation pathways, and that a limited number of mutations was sufficient for neutralizing activity. Our study suggests multiple B cell evolutionary pathways leading to DENV bNAbs targeting a new epitope that can be exploited for vaccine design.

DisruPPI: structure-based computational redesign algorithm for protein binding disruption

Motivation

Disruption of protein–protein interactions can mitigate antibody recognition of therapeutic proteins, yield monomeric forms of oligomeric proteins, and elucidate signaling mechanisms, among other applications. While designing affinity-enhancing mutations remains generally quite challenging, both statistically and physically based computational methods can precisely identify affinity-reducing mutations. In order to leverage this ability to design variants of a target protein with disrupted interactions, we developed the DisruPPI protein design method (DISRUpting Protein–Protein Interactions) to optimize combinations of mutations simultaneously for both disruption and stability, so that incorporated disruptive mutations do not inadvertently affect the target protein adversely.

Results

Two existing methods for predicting mutational effects on binding, FoldX and INT5, were demonstrated to be quite precise in selecting disruptive mutations from the SKEMPI and AB-Bind databases of experimentally determined changes in binding free energy. DisruPPI was implemented to use an INT5-based disruption score integrated with an AMBER-based stability assessment and was applied to disrupt protein interactions in a set of different targets representing diverse applications. In retrospective evaluation with three different case studies, comparison of DisruPPI-designed variants to published experimental data showed that DisruPPI was able to identify more diverse interaction-disrupting and stability-preserving variants more efficiently and effectively than previous approaches. In prospective application to an interaction between enhanced green fluorescent protein (EGFP) and a nanobody, DisruPPI was used to design five EGFP variants, all of which were shown to have significantly reduced nanobody binding while maintaining function and thermostability. This demonstrates that DisruPPI may be readily utilized for effective removal of known epitopes of therapeutically relevant proteins.

Availability and implementation

DisruPPI is implemented in the EpiSweep package, freely available under an academic use license.

Supplementary information

Supplementary data are available at Bioinformatics online.

THETA system allows one-step isolation of tagged proteins through temperature-dependent protein-peptide interaction

Tools to control protein-protein interactions by external stimuli have been extensively developed. For this purpose, thermal stimulation can be utilized in addition to light. In this study, we identify a monoclonal antibody termed C13 mAb, which shows an approximately 480-fold decrease in the affinity constant at 37 °C compared to that at 4 °C. Next, we apply this temperature-dependent protein-peptide interaction for one-step protein purifications. We term this THermal-Elution-based TAg system as the THETA system, in which gel-immobilized C13 mAb-derived single-chain variable fragment (scFv) (termed THETAL) is able to bind with proteins tagged by C13 mAb-epitope(s) (THETAS) at 4 °C and thermally release at 37-42 °C. Moreover, to reveal the temperature-dependent interaction mechanism, molecular dynamics simulations are performed along with epitope mapping experiments. Overall, the high specificity and reversibility of the temperature-dependent features of the THETA system will support a wide variety of future applications such as thermogenetics.

Structural Vaccinology for Viral Vaccine Design

Although vaccines have proven pivotal against arrays of infectious viral diseases, there are still no effective vaccines against many viruses. New structural insights into the viral envelope, protein conformation, and antigenic epitopes can guide the design of novel vaccines against challenging viruses such as human immunodeficiency virus (HIV), hepatitis C virus, enterovirus A71, and dengue virus. Recent studies demonstrated that applications of this structural information can solve some of the vaccine conundrums. This review focuses on recent advances in structure-based vaccine design, or structural vaccinology, for novel and innovative viral vaccine design.

Designing antibody against highly conserved region of dengue envelope protein by in silico screening of scFv mutant library

Dengue being one of the deadliest diseases of tropical regions, enforces to put continuous efforts for the development of vaccine and effective therapeutics. Most of the antibodies generated during dengue infection are non-neutralizing and cause antibody dependent enhancement. Hence, making a potent neutralizing antibody against all four dengue serotypes could be very effective for the treatment. However, designing a single antibody for all serotypes is difficult due to variation in protein sequences. Therefore, the objective is to identify conserved region of dengue envelope protein and then develop an antibody against that conserved region. Before advancing to the development of such an antibody, it is desirable to validate the interactions between antibody and dengue envelope protein. In silico analysis of such interactions provides a good platform to find out a suitable region to design and construct an antibody against it by analyzing antigen-antibody interaction before synthesizing the antibody. In this study, two highly conserved regions of dengue envelope protein were identified and an scFv was constructed against it. Both scFv and FuBc proteins were expressed in bacterial expression system and binding efficiency was analyzed by SPR analysis with KD value 2.3 μM. In order to improve binding efficiency, an in silico scFv mutant library was created which was virtually screened for higher binding efficiency. Six mutants with high binding efficiency were selected for further analysis. The binding ability of these mutants were predicted using simulation analysis which shows these mutations were stabilizing scFv-FuBc complex.

Rational Engineering and Characterization of an mAb that Neutralizes Zika Virus by Targeting a Mutationally Constrained Quaternary Epitope

Following the recent emergence of Zika virus (ZIKV), many murine and human neutralizing anti-ZIKV antibodies have been reported. Given the risk of virus escape mutants, engineering antibodies that target mutationally constrained epitopes with therapeutically relevant potencies can be valuable for combating future outbreaks. Here, we applied computational methods to engineer an antibody, ZAb_FLEP, that targets a highly networked and therefore mutationally constrained surface formed by the envelope protein dimer. ZAb_FLEP neutralized a breadth of ZIKV strains and protected mice in distinct in vivo models, including resolving vertical transmission and fetal mortality in infected pregnant mice. Serial passaging of ZIKV in the presence of ZAb_FLEP failed to generate viral escape mutants, suggesting that its epitope is indeed mutationally constrained. A single-particle cryo-EM reconstruction of the Fab-ZIKV complex validated the structural model and revealed insights into ZAb_FLEP's neutralization mechanism. ZAb_FLEP has potential as a therapeutic in future outbreaks.

Engineered Dengue Virus Domain III Proteins Elicit Cross-Neutralizing Antibody Responses in Mice

Dengue virus is the most globally prevalent mosquito-transmitted virus. Primary infection with one of four cocirculating serotypes (DENV-1 to -4) causes a febrile illness, but secondary infection with a heterologous serotype can result in severe disease, due in part to antibody-dependent enhancement of infection (ADE). In ADE, cross-reactive but nonneutralizing antibodies, or subprotective levels of neutralizing antibodies, promote uptake of antibody-opsonized virus in Fc-γ receptor-positive cells. Thus, elicitation of broadly neutralizing antibodies (bNAbs), but not nonneutralizing antibodies, is desirable for dengue vaccine development. Domain III of the envelope glycoprotein (EDIII) is targeted by bNAbs and thus is an attractive immunogen. However, immunization with EDIII results in sera with limited neutralization breadth. We developed "resurfaced" EDIII immunogens (rsDIIIs) in which the A/G strand epitope that is targeted by bNAb 4E11 is maintained but less desirable epitopes are masked. RsDIIIs bound 4E11, but not serotype-specific or nonneutralizing antibodies. One rsDIII and, unexpectedly, wild-type (WT) DENV-2 EDIII elicited cross-neutralizing antibody responses against DENV-1 to -3 in mice. While these sera were cross-neutralizing, they were not sufficiently potent to protect AG129 immunocompromised mice at a dose of 200 μl (50% focus reduction neutralization titer [FRNT₅₀], ∼1:60 to 1:130) against mouse-adapted DENV-2. Our results provide insight into immunogen design strategies based on EDIII.IMPORTANCE Dengue virus causes approximately 390 million infections per year. Primary infection by one serotype causes a self-limiting febrile illness, but secondary infection by a heterologous serotype can result in severe dengue syndrome, which is characterized by hemorrhagic fever and shock syndrome. This severe disease is thought to arise because of cross-reactive, non- or poorly neutralizing antibodies from the primary infection that are present in serum at the time of secondary infection. These cross-reactive antibodies enhance the infection rather than controlling it. Therefore, induction of a broadly and potently neutralizing antibody response is desirable for dengue vaccine development. Here, we explore a novel strategy for developing immunogens based on domain III of the E glycoprotein, where undesirable epitopes (nonneutralizing or nonconserved) are masked by mutation. This work provides fundamental insight into the immune response to domain III that can be leveraged for future immunogen design.

In Silico Methods in Antibody Design

Antibody therapies with high efficiency and low toxicity are becoming one of the major approaches in antibody therapeutics. Based on high-throughput sequencing and increasing experimental structures of antibodies/antibody-antigen complexes, computational approaches can predict antibody/antigen structures, engineering the function of antibodies and design antibody-antigen complexes with improved properties. This review summarizes recent progress in the field of in silico design of antibodies, including antibody structure modeling, antibody-antigen complex prediction, antibody stability evaluation, and allosteric effects in antibodies and functions. We listed the cases in which these methods have helped experimental studies to improve the affinities and physicochemical properties of antibodies. We emphasized how the molecular dynamics unveiled the allosteric effects during antibody-antigen recognition and antibody-effector recognition.

Molecular basis for dengue virus broad cross-neutralization by humanized monoclonal antibody 513

Dengue is a widespread viral disease with 3.6 billion people at risk worldwide. Humanized monoclonal antibody (mAb) 513, currently undergoing clinical trials in Singapore, targets an epitope on the envelope protein domain III exposed at the surface of the viral particle. This antibody potently neutralizes all four dengue virus serotypes in a humanized mouse model that recapitulates human dengue infection, without signs of antibody-mediated enhancement of the disease. The crystal structure of single-chain variable fragment (scFv) 513 bound to the envelope protein domain III from dengue virus serotype 4 was used as a template to explore the molecular origins of the broader cross-reactivity and increased in vivo potency of mAb 513, compared to the parent murine mAb 4E11, using molecular dynamics simulations and network analyses. These two methods are a powerful complement to existing structural and binding data and detail specific interactions that underpin the differential binding of the two antibodies. We found that a Glu at position H55 (Glu^H55) from the second Complementarity Determining Region of the Heavy chain (CDR-H2) which corresponds to Ala in 4E11, is a major contributor to the enhancement in the interactions of mAb 513 compared to 4E11. Importantly, we also validate the importance of Glu^H55 using site-directed mutagenesis followed by isothermal titration calorimetry measurements.

Computational Modeling of Hepatitis C Virus Envelope Glycoprotein Structure and Recognition

Hepatitis C virus (HCV) is a major global health concern, and though therapeutic options have improved, no vaccine is available despite decades of research. As HCV can rapidly mutate to evade the immune response, an effective HCV vaccine must rely on identification and characterization of sites critical for broad immune protection and viral neutralization. This knowledge depends on structural and mechanistic insights of the E1 and E2 envelope glycoproteins, which assemble as a heterodimer on the surface of the virion, engage coreceptors during host cell entry, and are the primary targets of antibodies. Due to the challenges in determining experimental structures, structural information on E1 and E2 and their interaction is relatively limited, providing opportunities to model the structures, interactions, and dynamics of these proteins. This review highlights efforts to model the E2 glycoprotein structure, the assembly of the functional E1E2 heterodimer, the structure and binding of human coreceptors, and recognition by key neutralizing antibodies. We also discuss a comparison of recently described models of full E1E2 heterodimer structures, a simulation of the dynamics of key epitope sites, and modeling glycosylation. These modeling efforts provide useful mechanistic hypotheses for further experimental studies of HCV envelope assembly, recognition, and viral fitness, and underscore the benefit of combining experimental and computational modeling approaches to reveal new insights. Additionally, computational design approaches have produced promising candidates for epitope-based vaccine immunogens that specifically target key epitopes, providing a possible avenue to optimize HCV vaccines versus using native glycoproteins. Advancing knowledge of HCV envelope structure and immune recognition is highly applicable toward the development of an effective vaccine for HCV and can provide lessons and insights relevant to modeling and characterizing other viruses.

Development of Antibody Therapeutics against Flaviviruses

Recent outbreaks of Zika virus (ZIKV) highlight the urgent need to develop efficacious interventions against flaviviruses, many of which cause devastating epidemics around the world. Monoclonal antibodies (mAb) have been at the forefront of treatment for cancer and a wide array of other diseases due to their specificity and potency. While mammalian cell-produced mAbs have shown promise as therapeutic candidates against several flaviviruses, their eventual approval for human application still faces several challenges including their potential risk of predisposing treated patients to more severe secondary infection by a heterologous flavivirus through antibody-dependent enhancement (ADE). The high cost associated with mAb production in mammalian cell cultures also poses a challenge for the feasible application of these drugs to the developing world where the majority of flavivirus infection occurs. Here, we review the current therapeutic mAb candidates against various flaviviruses including West Nile (WNV), Dengue virus (DENV), and ZIKV. The progress of using plants for developing safer and more economical mAb therapeutics against flaviviruses is discussed within the context of their expression, characterization, downstream processing, neutralization, and in vivo efficacy. The progress of using plant glycoengineering to address ADE, the major impediment of flavivirus therapeutic development, is highlighted. These advancements suggest that plant-based systems are excellent alternatives for addressing the remaining challenges of mAb therapeutic development against flavivirus and may facilitate the eventual commercialization of these drug candidates.

Structural and Functional Characterization of a Cross-Reactive Dengue Virus Neutralizing Antibody that Recognizes a Cryptic Epitope

Understanding the molecular basis of the neutralizing antibody response to dengue virus (DENV) is an essential component in the design and development of effective vaccines and immunotherapeutics. Here we present the structure of a cross-reactive, neutralizing antibody, 3E31, in complex with domain III (DIII) of the DENV envelope (E) protein and reveal a conserved, temperature-sensitive, cryptic epitope on DIII that is not available in any of the known conformations of E on the dengue virion. We observed that 3E31 inhibits E-mediated membrane fusion, suggesting that the antibody is able to neutralize virus through binding an as-yet uncharacterized intermediate conformation of DENV E and sterically block trimer formation. Finally, we show that, unlike cross-reactive fusion peptide-specific antibodies, 3E31 does not promote antibody-dependent enhancement of infection at sub-neutralizing concentrations. Our results highlight the 3E31 epitope on the E protein DIII as a promising target for immunotherapeutics or vaccine design.

Arginine mutations in antibody complementarity-determining regions display context-dependent affinity/specificity trade-offs

Antibodies commonly accumulate charged mutations in their complementarity-determining regions (CDRs) during affinity maturation to enhance electrostatic interactions. However, charged mutations can mediate non-specific interactions, and it is unclear to what extent CDRs can accumulate charged residues to increase antibody affinity without compromising specificity. This is especially concerning for positively charged CDR mutations that are linked to antibody polyspecificity. To better understand antibody affinity/specificity trade-offs, we have selected single-chain antibody fragments specific for the negatively charged and hydrophobic Alzheimer's amyloid β peptide using weak and stringent selections for antibody specificity. Antibody variants isolated using weak selections for specificity were enriched in arginine CDR mutations and displayed low specificity. Alanine-scanning mutagenesis revealed that the affinities of these antibodies were strongly dependent on their arginine mutations. Antibody variants isolated using stringent selections for specificity were also enriched in arginine CDR mutations, but these antibodies possessed significant improvements in specificity. Importantly, the affinities of the most specific antibodies were much less dependent on their arginine mutations, suggesting that over-reliance on arginine for affinity leads to reduced specificity. Structural modeling and molecular simulations reveal unique hydrophobic environments near the arginine CDR mutations. The more specific antibodies contained arginine mutations in the most hydrophobic portions of the CDRs, whereas the less specific antibodies contained arginine mutations in more hydrophilic regions. These findings demonstrate that arginine mutations in antibody CDRs display context-dependent impacts on specificity and that affinity/specificity trade-offs are governed by the relative contribution of arginine CDR residues to the overall antibody affinity.

Identification and characterization of a neutralizing monoclonal antibody that provides complete protection against Yersinia pestis

Yersinia pestis (Y. pestis) has caused an alarming number of deaths throughout recorded human history, and novel prophylactics and therapeutics are necessary given its potential as a bioweapon. Only one monoclonal antibody has been identified to date that provides complete protection against Y. pestis. Here, we describe a second novel murine monoclonal antibody (F2H5) that provided complete protection against Y. pestis 141 infection when administered prophylactically to Balb/c mice (100 μg intravenously). We humanized F2H5, characterized its ability to bind to the Y. pestis F1 protein and further characterized the neutralizing epitope using computational and experimental approaches. While Western blot results suggested a linear epitope, peptide mapping using ELISA failed to identify an epitope, suggesting a conformational epitope instead. We adopted a computational approach based on Residue Contact Frequency to predict the site of antigen-antibody interaction and defined the F2H5/F1 binding site computationally. Based on computational approach, we determined that residues G104E105N106 in F1 were critical to F2H5 binding and that CDRH2 and CDRH3 of F2H5 interacted with F1. Our results show that combining computational approach and experimental approach can effectively identify epitopes.

Temperature-dependent folding allows stable dimerization of secretory and virus-associated E proteins of Dengue and Zika viruses in mammalian cells

Dengue and Zika are two of the most important human viral pathogens worldwide. In both cases, the envelope glycoprotein E is the main target of the antibody response. Recently, new complex quaternary epitopes were identified which are the consequence of the arrangement of the antiparallel E dimers on the viral surface. Such epitopes can be exploited to develop more efficient cross-neutralizing vaccines. Here we describe a successful covalent stabilization of E dimers from Dengue and Zika viruses in mammalian cells. Folding and dimerization of secretory E was found to be strongly dependent on temperature but independent of PrM co-expression. In addition, we found that, due to the close relationship between flaviviruses, Dengue and Zika viruses E proteins can form heterodimers and assemble into mosaic viral particles. Finally, we present new virus-free analytical platforms to study and screen antibody responses against Dengue and Zika, which allow for differentiation of epitopes restricted to specific domains, dimers and higher order arrangements of E.

Computational design of an epitope-specific Keap1 binding antibody using hotspot residues grafting and CDR loop swapping

Therapeutic and diagnostic applications of monoclonal antibodies often require careful selection of binders that recognize specific epitopes on the target molecule to exert a desired modulation of biological function. Here we present a proof-of-concept application for the rational design of an epitope-specific antibody binding with the target protein Keap1, by grafting pre-defined structural interaction patterns from the native binding partner protein, Nrf2, onto geometrically matched positions of a set of antibody scaffolds. The designed antibodies bind to Keap1 and block the Keap1-Nrf2 interaction in an epitope-specific way. One resulting antibody is further optimised to achieve low-nanomolar binding affinity by in silico redesign of the CDRH3 sequences. An X-ray co-crystal structure of one resulting design reveals that the actual binding orientation and interface with Keap1 is very close to the design model, despite an unexpected CDRH3 tilt and V_H/V_L interface deviation, which indicates that the modelling precision may be improved by taking into account simultaneous CDR loops conformation and V_H/V_L orientation optimisation upon antibody sequence change. Our study confirms that, given a pre-existing crystal structure of the target protein-protein interaction, hotspots grafting with CDR loop swapping is an attractive route to the rational design of an antibody targeting a pre-selected epitope.

A potent neutralizing antibody with therapeutic potential against all four serotypes of dengue virus

A therapy for dengue is still elusive. We describe the neutralizing and protective capacity of a dengue serotype-cross-reactive antibody isolated from the plasmablasts of a patient. Antibody SIgN-3C neutralized all four dengue virus serotypes at nano to picomolar concentrations and significantly decreased viremia of all serotypes in adult mice when given 2 days after infection. Moreover, mice were protected from pathology and death from a lethal dengue virus-2 infection. To avoid potential Fc-mediated uptake of immune complexes and ensuing enhanced infection, we introduced a LALA mutation in the Fc part. SIgN-3C-LALA was as efficient as the non-modified antibody in neutralizing dengue virus and in protecting mice while antibody-dependent enhancement was completely abrogated. The epitope of the antibody includes conserved amino acids in all three domains of the glycoprotein, which can explain its cross-reactivity. SIgN-3C-LALA neutralizes dengue virus both pre and post-attachment to host cells. These attributes likely contribute to the remarkable protective capacity of SIgN-3C.

Dengue: an update on treatment options

Dengue is the most important mosquito-borne viral pathogen globally, with approximately 100 million cases of acute dengue annually. Infection can result in severe, life-threatening disease. Currently, there is no effective vaccine or licensed antiviral. Management is primarily supportive with fluids. Direct antiviral therapies that reduce dengue severity could be useful although these would need to inhibit all four viral serotypes effectively. This review focuses on the interventions that currently considered the gold standard in case management as well as exploratory therapies that have been studied in clinical trials. Although antiviral drug and therapeutic antibodies for dengue remain a work in progress, these studies have produced some promising results and may have the potential to be future drugs.

Comprehensive mapping of functional epitopes on dengue virus glycoprotein E DIII for binding to broadly neutralizing antibodies 4E11 and 4E5A by phage display

Here we investigated the binding of Dengue virus envelope glycoprotein domain III (DIII) by two broadly neutralizing antibodies (bNAbs), 4E11 and 4E5A. There are four serotypes of Dengue virus (DENV-1 to -4), whose DIII sequences vary by up to 49%. We used combinatorial alanine scanning mutagenesis, a phage display approach, to map functional epitopes (those residues that contribute most significantly to the energetics of antibody-antigen interaction) on these four serotypes. Our results showed that 4E11, which binds strongly to DENV-1, -2, and -3, and moderately to DENV-4, recognized a common conserved core functional epitope involving DIII residues K310, L/I387, L389, and W391. There were also unique recognition features for each serotype, suggesting that 4E11 has flexible recognition requirements. Similar scanning studies for the related bNAb 4E5A, which binds more tightly to DENV-4, identified broader functional epitopes on DENV-1. These results provide useful information for immunogen and therapeutic antibody design.

A broadly neutralizing human monoclonal antibody is effective against H7N9

Emerging strains of influenza represent a significant public health threat with potential pandemic consequences. Of particular concern are the recently emerged H7N9 strains which cause pneumonia with acute respiratory distress syndrome. Estimates are that nearly 80% of hospitalized patients with H7N9 have received intensive care unit support. VIS410, a human antibody, targets a unique conserved epitope on influenza A. We evaluated the efficacy of VIS410 for neutralization of group 2 influenza strains, including H3N2 and H7N9 strains in vitro and in vivo. VIS410, administered at 50 mg/kg, protected DBA mice infected with A/Anhui/2013 (H7N9), resulting in significant survival benefit upon single-dose (-24 h) or double-dose (-12 h, +48 h) administration (P < 0.001). A single dose of VIS410 at 50 mg/kg (-12 h) combined with oseltamivir at 50 mg/kg (-12 h, twice daily for 7 d) in C57BL/6 mice infected with A/Shanghai 2/2013 (H7N9) resulted in significant decreased lung viral load (P = 0.002) and decreased lung cytokine responses for nine of the 11 cytokines measured. Based on these results, we find that VIS410 may be effective either as monotherapy or combined with antivirals in treating H7N9 disease, as well as disease from other influenza strains.

Advances in Antibody Design

The use of monoclonal antibodies as therapeutics requires optimizing several of their key attributes. These include binding affinity and specificity, folding stability, solubility, pharmacokinetics, effector functions, and compatibility with the attachment of additional antibody domains (bispecific antibodies) and cytotoxic drugs (antibody-drug conjugates). Addressing these and other challenges requires the use of systematic design methods that complement powerful immunization and in vitro screening methods. We review advances in designing the binding loops, scaffolds, domain interfaces, constant regions, post-translational and chemical modifications, and bispecific architectures of antibodies and fragments thereof to improve their bioactivity. We also highlight unmet challenges in antibody design that must be overcome to generate potent antibody therapeutics.

Structure-Guided Design of an Anti-dengue Antibody Directed to a Non-immunodominant Epitope

Dengue is the most common vector-borne viral disease, causing nearly 400 million infections yearly. Currently there are no approved therapies. Antibody epitopes that elicit weak humoral responses may not be accessible by conventional B cell panning methods. To demonstrate an alternative strategy to generating a therapeutic antibody, we employed a non-immunodominant, but functionally relevant, epitope in domain III of the E protein, and engineered by structure-guided methods an antibody directed to it. The resulting antibody, Ab513, exhibits high-affinity binding to, and broadly neutralizes, multiple genotypes within all four serotypes. To assess therapeutic relevance of Ab513, activity against important human clinical features of dengue was investigated. Ab513 mitigates thrombocytopenia in a humanized mouse model, resolves vascular leakage, reduces viremia to nearly undetectable levels, and protects mice in a maternal transfer model of lethal antibody-mediated enhancement. The results demonstrate that Ab513 may reduce the public health burden from dengue.

Inferring the microscopic surface energy of protein-protein interfaces from mutation data

Mutations at protein-protein recognition sites alter binding strength by altering the chemical nature of the interacting surfaces. We present a simple surface energy model, parameterized with empirical ΔΔG values, yielding mean energies of -48 cal mol(-1) Å(-2) for interactions between hydrophobic surfaces, -51 to -80 cal mol(-1) Å(-2) for surfaces of complementary charge, and 66-83 cal mol(-1) Å(-2) for electrostatically repelling surfaces, relative to the aqueous phase. This places the mean energy of hydrophobic surface burial at -24 cal mol(-1) Å(-2) . Despite neglecting configurational entropy and intramolecular changes, the model correlates with empirical binding free energies of a functionally diverse set of rigid-body interactions (r = 0.66). When used to rerank docking poses, it can place near-native solutions in the top 10 for 37% of the complexes evaluated, and 82% in the top 100. The method shows that hydrophobic burial is the driving force for protein association, accounting for 50-95% of the cohesive energy. The model is available open-source from http://life.bsc.es/pid/web/surface_energy/ and via the CCharpPPI web server http://life.bsc.es/pid/ccharppi/.

Antibody informatics for drug discovery

More and more antibody therapeutics are being approved every year, mainly due to their high efficacy and antigen selectivity. However, it is still difficult to identify the antigen, and thereby the function, of an antibody if no other information is available. There are obstacles inherent to the antibody science in every project in antibody drug discovery. Recent experimental technologies allow for the rapid generation of large-scale data on antibody sequences, affinity, potency, structures, and biological functions; this should accelerate drug discovery research. Therefore, a robust bioinformatic infrastructure for these large data sets has become necessary. In this article, we first identify and discuss the typical obstacles faced during the antibody drug discovery process. We then summarize the current status of three sub-fields of antibody informatics as follows: (i) recent progress in technologies for antibody rational design using computational approaches to affinity and stability improvement, as well as ab-initio and homology-based antibody modeling; (ii) resources for antibody sequences, structures, and immune epitopes and open drug discovery resources for development of antibody drugs; and (iii) antibody numbering and IMGT. Here, we review "antibody informatics," which may integrate the above three fields so that bridging the gaps between industrial needs and academic solutions can be accelerated. This article is part of a Special Issue entitled: Recent advances in molecular engineering of antibody.

Discovery of protective B-cell epitopes for development of antimicrobial vaccines and antibody therapeutics

Protective antibodies play an essential role in immunity to infection by neutralizing microbes or their toxins and recruiting microbicidal effector functions. Identification of the protective B-cell epitopes, those parts of microbial antigens that contact the variable regions of the protective antibodies, can lead to development of antibody therapeutics, guide vaccine design, enable assessment of protective antibody responses in infected or vaccinated individuals, and uncover or localize pathogenic microbial functions that could be targeted by novel antimicrobials. Monoclonal antibodies are required to link in vivo or in vitro protective effects to specific epitopes and may be obtained from experimental animals or from humans, and their binding can be localized to specific regions of antigens by immunochemical assays. The epitopes are then identified with mapping methods such as X-ray crystallography of antigen-antibody complexes, antibody inhibition of hydrogen-deuterium exchange in the antigen, antibody-induced alteration of the nuclear magnetic resonance spectrum of the antigen, and experimentally validated computational docking of antigen-antibody complexes. The diversity in shape, size and structure of protective B-cell epitopes, and the increasing importance of protective B-cell epitope discovery to development of vaccines and antibody therapeutics are illustrated through examples from different microbe categories, with emphasis on epitopes targeted by broadly neutralizing antibodies to pathogens of high antigenic variation. Examples include the V-shaped Ab52 glycan epitope in the O-antigen of Francisella tularensis, the concave CR6261 peptidic epitope in the haemagglutinin stem of influenza virus H1N1, and the convex/concave PG16 glycopeptidic epitope in the gp120 V1/V2 loop of HIV type 1.

Energetic and dynamic aspects of the affinity maturation process: characterizing improved variants from the bevacizumab antibody with molecular simulations

Antibody affinity maturation is one of the fundamental processes of immune defense against invading pathogens. From the biological point of view, the clonal selection hypothesis represents the most accepted mechanism to explain how mutations increasing the affinity for target antigens are introduced and selected in antibody molecules. However, understanding at the molecular level how protein modifications, such as point mutation, can modify and modulate the affinity of an antibody for its antigen is still a major open issue in molecular biology. In this paper, we address various aspects of this problem by analyzing and comparing atomistic simulations of 17 variants of the bevacizumab antibody, all directed against the common target protein VEGF-A. In particular, we examine MD-based descriptors of the internal energetics and dynamics of mutated antibodies and their possible correlations with experimentally determined affinities for the antigens. Our results show that affinity improvement is correlated with a variation of the internal stabilization energy of the antibody molecule when bound to the antigen, compensated by the variation in the interaction energy between the antigen and the antibody, paralleled by an overall modulation of internal coordination within the antibody molecular structure. A possible model of the mechanism of rigidification and of the main residues involved is proposed. Overall, our results can help in understanding the molecular determinants of antigen recognition and have implications in the rational design of new antibodies with optimized affinities.

Protein engineering strategies for the development of viral vaccines and immunotherapeutics

Vaccines that elicit a protective broadly neutralizing antibody (bNAb) response and monoclonal antibody therapies are critical for the treatment and prevention of viral infections. However, isolation of protective neutralizing antibodies has been challenging for some viruses, notably those with high antigenic diversity or those that do not elicit a bNAb response in the course of natural infection. Here, we discuss recent work that employs protein engineering strategies to design immunogens that elicit bNAbs or engineer novel bNAbs. We highlight the use of rational, computational, and combinatorial strategies and assess the potential of these approaches for the development of new vaccines and immunotherapeutics.

Therapeutic antibodies as a treatment option for dengue fever

Dengue fever is the most prevalent mosquito-borne viral disease globally with about 100 million cases of acute dengue annually. Severe dengue infection can result in a life-threatening illness. In the absence of either a licensed vaccine or antiviral drug against dengue, therapeutic antibodies that neutralize dengue virus (DENV) may serve as an effective medical countermeasure against severe dengue. However, therapeutic antibodies would need to effectively neutralize all four DENV serotypes. It must not induce antibody-dependent enhancement of DENV infection in monocytes/macrophages through Fc gamma receptor (FcγR)-mediated phagocytosis, which is hypothesized to increase the risk of severe dengue. Here, we review the strategies and technologies that can be adopted to develop antibodies for therapeutic applications. We also discuss the mechanism of antibody neutralization in the cells targeted by DENV that express Fc gamma receptor. These studies have provided significant insight toward the use of therapeutic antibodies as a potentially promising bulwark against dengue.

Scoring functions for protein-protein interactions

The computational evaluation of protein-protein interactions will play an important role in organising the wealth of data being generated by high-throughput initiatives. Here we discuss future applications, report recent developments and identify areas requiring further investigation. Many functions have been developed to quantify the structural and energetic properties of interacting proteins, finding use in interrelated challenges revolving around the relationship between sequence, structure and binding free energy. These include loop modelling, side-chain refinement, docking, multimer assembly, affinity prediction, affinity change upon mutation, hotspots location and interface design. Information derived from models optimised for one of these challenges can be used to benefit the others, and can be unified within the theoretical frameworks of multi-task learning and Pareto-optimal multi-objective learning.