Role of framework mutations and antibody flexibility in the evolution of broadly neutralizing antibodies

TEMPRO: nanobody melting temperature estimation model using protein embeddings

Single-domain antibodies (sdAbs) or nanobodies have received widespread attention due to their small size (~ 15 kDa) and diverse applications in bio-derived therapeutics. As many modern biotechnology breakthroughs are applied to antibody engineering and design, nanobody thermostability or melting temperature (Tm) is crucial for their successful utilization. In this study, we present TEMPRO which is a predictive modeling approach for estimating the Tm of nanobodies using computational methods. Our methodology integrates various nanobody biophysical features to include Evolutionary Scale Modeling (ESM) embeddings, NetSurfP3 structural predictions, pLDDT scores per sdAb region from AlphaFold2, and each sequence's physicochemical characteristics. This approach is validated with our combined dataset containing 567 unique sequences with corresponding experimental Tm values from a manually curated internal data and a recently published nanobody database, NbThermo. Our results indicate the efficacy of protein embeddings in reliably predicting the Tm of sdAbs with mean absolute error (MAE) of 4.03 °C and root mean squared error (RMSE) of 5.66 °C, thus offering a valuable tool for the optimization of nanobodies for various biomedical and therapeutic applications. Moreover, we have validated the models' performance using experimentally determined Tms from nanobodies not found in NbThermo. This predictive model not only enhances nanobody thermostability prediction, but also provides a useful perspective of using embeddings as a tool for facilitating a broader applicability of downstream protein analyses.

Mechanistic Insights into How the Single Point Mutation Change the Autoantibody Repertoire

A recent study showed that just one point mutation F33 to Y in the complementarity-determining region 1 of heavy chain (H-CDR1) could lead to the auto-antibody losing its DNA binding ability. However, the potential molecular mechanisms have not been well elucidated. In this study, we investigated how the antibody lost the DNA binding ability caused by mutation F33 to Y in the H-CDR1. We found that the electrostatic force was not the primary driving force for the interaction between anti-DNA antibodies and the antigen single strand DNA (ssDNA), and that the H-CDR2 largely contributed to the binding of antigen ssDNA, even larger than H-CDR1. The H-F33Y mutation could increase the hydrogen-bond interaction but impair the pi-pi stacking interaction between the antibody and ssDNA. We further found that F33H, W98H and Y95L in the wiletype antibody could form the stable pi-pi stacking interaction with the nucleotide bases of ssDNA. However, the Y33 in mutant could not form the parallel sandwich pi-pi stacking interaction with the ssDNA. To further confirm the importance of pi-pi stacking, the wildtype antibody and the mutants (F33YH, F33AH, W98AH and Y95AL) were experimentally expressed in CHO cells and purified, and the results from ELISA clearly showed that all the mutants lost the ssDNA binding ability. Taken together, our findings may not only deepen the understanding of the underlying interaction mechanism between autoantibody and antigen, but also broad implications in the field of antibody engineer.

Humanization of Pan-HLA-DR mAb 44H10 Hinges on Critical Residues in the Antibody Framework

Reducing the immunogenicity of animal-derived monoclonal antibodies (mAbs) for use in humans is critical to maximize therapeutic effectiveness and preclude potential adverse events. While traditional humanization methods have primarily focused on grafting antibody Complementarity-Determining Regions (CDRs) on homologous human antibody scaffolds, framework regions can also play essential roles in antigen binding. Here, we describe the humanization of the pan-HLA-DR mAb 44H10, a murine antibody displaying significant involvement of the framework region in antigen binding. Using a structure-guided approach, we identify and restore framework residues that directly interact with the antigen or indirectly modulate antigen binding by shaping the antibody paratope and engineer a humanized antibody with affinity, biophysical profile, and molecular binding basis comparable to that of the parental 44H10 mAb. As a humanized molecule, this antibody holds promise as a scaffold for the development of MHC class II-targeting therapeutics and vaccines.

Effects of N-glycans on the structure of human IgA2

The transition of IgA antibodies into clinical development is crucial because they have the potential to create a new class of therapeutics with superior pathogen neutralization, cancer cell killing, and immunomodulation capacity compared to IgG. However, the biological role of IgA glycans in these processes needs to be better understood. This study provides a detailed biochemical, biophysical, and structural characterization of recombinant monomeric human IgA2, which varies in the amount/locations of attached glycans. Monomeric IgA2 antibodies were produced by removing the N-linked glycans in the CH1 and CH2 domains. The impact of glycans on oligomer formation, thermal stability, and receptor binding was evaluated. In addition, we performed a structural analysis of recombinant IgA2 in solution using Small Angle X-Ray Scattering (SAXS) to examine the effect of glycans on protein structure and flexibility. Our results indicate that the absence of glycans in the Fc tail region leads to higher-order aggregates. SAXS, combined with atomistic modeling, showed that the lack of glycans in the CH2 domain results in increased flexibility between the Fab and Fc domains and a different distribution of open and closed conformations in solution. When binding with the Fcα-receptor, the dissociation constant remains unaltered in the absence of glycans in the CH1 or CH2 domain, compared to the fully glycosylated protein. These results provide insights into N-glycans' function on IgA2, which could have important implications for developing more effective IgA-based therapeutics in the future.

Enhanced T cell receptor specificity through framework engineering

Development of T cell receptors (TCRs) as immunotherapeutics is hindered by inherent TCR cross-reactivity. Engineering more specific TCRs has proven challenging, as unlike antibodies, improving TCR affinity does not usually improve specificity. Although various protein design approaches have been explored to surmount this, mutations in TCR binding interfaces risk broadening specificity or introducing new reactivities. Here we explored if TCR specificity could alternatively be tuned through framework mutations distant from the interface. Studying the 868 TCR specific for the HIV SL9 epitope presented by HLA-A2, we used deep mutational scanning to identify a framework mutation above the mobile CDR3β loop. This glycine to proline mutation had no discernable impact on binding affinity or functional avidity towards the SL9 epitope but weakened recognition of SL9 escape variants and led to fewer responses in a SL9-derived positional scanning library. In contrast, an interfacial mutation near the tip of CDR3α that also did not impact affinity or functional avidity towards SL9 weakened specificity. Simulations indicated that the specificity-enhancing mutation functions by reducing the range of loop motions, limiting the ability of the TCR to adjust to different ligands. Although our results are likely to be TCR dependent, using framework engineering to control TCR loop motions may be a viable strategy for improving the specificity of TCR-based immunotherapies.

Next-generation bNAbs for HIV-1 cure strategies

Despite the ability to suppress viral replication using anti-retroviral therapy (ART), HIV-1 remains a global public health problem. Curative strategies for HIV-1 have to target and eradicate latently infected cells across the body, i.e. the viral reservoir. Broadly neutralizing antibodies (bNAbs) targeting the HIV-1 envelope glycoprotein (Env) have the capacity to neutralize virions and bind to infected cells to initiate elimination of these cells. To improve the efficacy of bNAbs in terms of viral suppression and viral reservoir eradication, next generation antibodies (Abs) are being developed that address the current limitations of Ab treatment efficacy; (1) low antigen (Env) density on (reactivated) HIV-1 infected cells, (2) high viral genetic diversity, (3) exhaustion of immune cells and (4) short half-life of Abs. In this review we summarize and discuss preclinical and clinical studies in which anti-HIV-1 Abs demonstrated potent viral control, and describe the development of engineered Abs that could address the limitations described above. Next generation Abs with optimized effector function, avidity, effector cell recruitment and immune cell activation have the potential to contribute to an HIV-1 cure or durable control.

Matrixed CDR grafting: A neoclassical framework for antibody humanization and developability

Discovery and optimization of a biotherapeutic monoclonal antibody requires a careful balance of target engagement and physicochemical developability properties. To take full advantage of the sequence diversity provided by different antibody discovery platforms, a rapid and reliable process for humanization of antibodies from nonhuman sources is required. Canonically, maximizing homology of the human variable region (V-region) to the original germline was believed to result in preservation of binding, often without much consideration for inherent molecular properties. We expand on this approach by grafting the complementary determining regions (CDRs) of a mouse anti-LAG3 antibody into an extensive matrix of human variable heavy chain (VH) and variable light chain (VL) framework regions with substantially broader sequence homology to assess the impact on complementary determining region-framework compatibility through progressive evaluation of expression, affinity, biophysical developability, and function. Specific VH and VL framework sequences were associated with major expression and purification phenotypes. Greater VL sequence conservation was correlated with retained or improved affinity. Analysis of grafts that bound the target demonstrated that initial developability criteria were significantly impacted by VH, but not VL. In contrast, cell binding and functional characteristics were significantly impacted by VL, but not VH. Principal component analysis of all factors identified multiple grafts that exhibited more favorable antibody properties, notably with nonoptimal sequence conservation. Overall, this study demonstrates that modern throughput systems enable a more thorough, customizable, and systematic analysis of graft-framework combinations, resulting in humanized antibodies with improved global properties that may progress through development more quickly and with a greater probability of success.

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.

High throughput analysis of B cell dynamics and neutralizing antibody development during immunization with a novel clade C HIV-1 envelope

A protective HIV-1 vaccine has been hampered by a limited understanding of how B cells acquire neutralizing activity. Our previous vaccines expressing two different HIV-1 envelopes elicited robust antigen specific serum IgG titers in 20 rhesus macaques; yet serum from only two animals neutralized the autologous virus. Here, we used high throughput immunoglobulin receptor and single cell RNA sequencing to characterize the overall expansion, recall, and maturation of antigen specific B cells longitudinally over 90 weeks. Diversification and expansion of many B cell clonotypes occurred broadly in the absence of serum neutralization. However, in one animal that developed neutralization, two neutralizing B cell clonotypes arose from the same immunoglobulin germline and were tracked longitudinally. Early antibody variants with high identity to germline neutralized the autologous virus while later variants acquired somatic hypermutation and increased neutralization potency. The early engagement of precursors capable of neutralization with little to no SHM followed by prolonged affinity maturation allowed the two neutralizing lineages to successfully persist despite many other antigen specific B cells. The findings provide new insight into B cells responding to HIV-1 envelope during heterologous prime and boost immunization in rhesus macaques and the development of selected autologous neutralizing antibody lineages.

Specific attributes of the VL domain influence both the structure and structural variability of CDR-H3 through steric effects

Antibodies, through their ability to target virtually any epitope, play a key role in driving the adaptive immune response in jawed vertebrates. The binding domains of standard antibodies are their variable light (VL) and heavy (VH) domains, both of which present analogous complementarity-determining region (CDR) loops. It has long been known that the VH CDRs contribute more heavily to the antigen-binding surface (paratope), with the CDR-H3 loop providing a major modality for the generation of diverse paratopes. Here, we provide evidence for an additional role of the VL domain as a modulator of CDR-H3 structure, using a diverse set of antibody crystal structures and a large set of molecular dynamics simulations. We show that specific attributes of the VL domain such as subtypes, CDR canonical forms and genes can influence the structural diversity of the CDR-H3 loop, and provide a physical model for how this effect occurs through inter-loop contacts and packing of CDRs against each other. Our results indicate that the rigid minor loops fine-tune the structure of CDR-H3, thereby contributing to the generation of surfaces complementary to the vast number of possible epitope topologies, and provide insights into the interdependent nature of CDR conformations, an understanding of which is important for the rational antibody design process.

Common framework mutations impact antibody interfacial dynamics and flexibility

Introduction

With the flood of engineered antibodies, there is a heightened need to elucidate the structural features of antibodies that contribute to specificity, stability, and breadth. While antibody flexibility and interface angle have begun to be explored, design rules have yet to emerge, as their impact on the metrics above remains unclear. Furthermore, the purpose of framework mutations in mature antibodies is highly convoluted.

Methods

To this end, a case study utilizing molecular dynamics simulations was undertaken to determine the impact framework mutations have on the VH-VL interface. We further sought to elucidate the governing mechanisms by which changes in the VH-VL interface angle impact structural elements of mature antibodies by looking at root mean squared deviations, root mean squared fluctuations, and solvent accessible surface area.

Results and discussion

Overall, our results suggest framework mutations can significantly shift the distribution of VH-VL interface angles, which leads to local changes in antibody flexibility through local changes in the solvent accessible surface area. The data presented herein highlights the need to reject the dogma of static antibody crystal structures and exemplifies the dynamic nature of these proteins in solution. Findings from this work further demonstrate the importance of framework mutations on antibody structure and lay the foundation for establishing design principles to create antibodies with increased specificity, stability, and breadth.

Hierarchical sequence-affinity landscapes shape the evolution of breadth in an anti-influenza receptor binding site antibody

Broadly neutralizing antibodies (bnAbs) that neutralize diverse variants of a particular virus are of considerable therapeutic interest. Recent advances have enabled us to isolate and engineer these antibodies as therapeutics, but eliciting them through vaccination remains challenging, in part due to our limited understanding of how antibodies evolve breadth. Here, we analyze the landscape by which an anti-influenza receptor binding site (RBS) bnAb, CH65, evolved broad affinity to diverse H1 influenza strains. We do this by generating an antibody library of all possible evolutionary intermediates between the unmutated common ancestor (UCA) and the affinity-matured CH65 antibody and measure the affinity of each intermediate to three distinct H1 antigens. We find that affinity to each antigen requires a specific set of mutations - distributed across the variable light and heavy chains - that interact non-additively (i.e., epistatically). These sets of mutations form a hierarchical pattern across the antigens, with increasingly divergent antigens requiring additional epistatic mutations beyond those required to bind less divergent antigens. We investigate the underlying biochemical and structural basis for these hierarchical sets of epistatic mutations and find that epistasis between heavy chain mutations and a mutation in the light chain at the V_H-V_L interface is essential for binding a divergent H1. Collectively, this is the first work to comprehensively characterize epistasis between heavy and light chain mutations and shows that such interactions are both strong and widespread. Together with our previous study analyzing a different class of anti-influenza antibodies, our results implicate epistasis as a general feature of antibody sequence-affinity landscapes that can potentiate and constrain the evolution of breadth.

B-cell epitope discovery: The first protein flexibility-based algorithm-Zika virus conserved epitope demonstration

Antibody-antigen interaction-at antigenic local environments called B-cell epitopes-is a prominent mechanism for neutralization of infection. Effective mimicry, and display, of B-cell epitopes is key to vaccine design. Here, a physical approach is evaluated for the discovery of epitopes which evolve slowly over closely related pathogens (conserved epitopes). The approach is 1) protein flexibility-based and 2) demonstrated with clinically relevant enveloped viruses, simulated via molecular dynamics. The approach is validated against 1) seven structurally characterized enveloped virus epitopes which evolved the least (out of thirty-nine enveloped virus-antibody structures), 2) two structurally characterized non-enveloped virus epitopes which evolved slowly (out of eight non-enveloped virus-antibody structures), and 3) eight preexisting epitope and peptide discovery algorithms. Rationale for a new benchmarking scheme is presented. A data-driven epitope clustering algorithm is introduced. The prediction of five Zika virus epitopes (for future exploration on recombinant vaccine technologies) is demonstrated. For the first time, protein flexibility is shown to outperform solvent accessible surface area as an epitope discovery metric.

The Effect of Treatment-Associated Mutations on HIV Replication and Transmission Cycles

HIV/AIDS mortality has been decreasing over the last decade. While promising, this decrease correlated directly with increased use of antiretroviral drugs. As a natural consequence of its high mutation rate, treatments provide selection pressure that promotes the natural selection of escape mutants. Individuals may acquire drug-naive strains, or those that have already mutated due to treatment. Even within a host, mutation affects HIV tropism, where initial infection begins with R5-tropic virus, but the clinical transition to AIDS correlates with mutations that lead to an X4-tropic switch. Furthermore, the high mutation rate of HIV has spelled failure for all attempts at an effective vaccine. Pre-exposure drugs are currently the most effective drug-based preventatives, but their effectiveness is also threatened by viral mutation. From attachment and entry to assembly and release, the steps in the replication cycle are also discussed to describe the drug mechanisms and mutations that arise due to those drugs. Revealing the patterns of HIV-1 mutations, their effects, and the coordinated attempt to understand and control them will lead to effective use of current preventative measures and treatment options, as well as the development of new ones.

A scalable model for simulating multi-round antibody evolution and benchmarking of clonal tree reconstruction methods

Affinity maturation (AM) of B cells through somatic hypermutations (SHMs) enables the immune system to evolve to recognize diverse pathogens. The accumulation of SHMs leads to the formation of clonal lineages of antibody-secreting b cells that have evolved from a common naïve B cell. Advances in high-throughput sequencing have enabled deep scans of B cell receptor repertoires, paving the way for reconstructing clonal trees. However, it is not clear if clonal trees, which capture microevolutionary time scales, can be reconstructed using traditional phylogenetic reconstruction methods with adequate accuracy. In fact, several clonal tree reconstruction methods have been developed to fix supposed shortcomings of phylogenetic methods. Nevertheless, no consensus has been reached regarding the relative accuracy of these methods, partially because evaluation is challenging. Benchmarking the performance of existing methods and developing better methods would both benefit from realistic models of clonal lineage evolution specifically designed for emulating B cell evolution. In this paper, we propose a model for modeling B cell clonal lineage evolution and use this model to benchmark several existing clonal tree reconstruction methods. Our model, designed to be extensible, has several features: by evolving the clonal tree and sequences simultaneously, it allows modeling selective pressure due to changes in affinity binding; it enables scalable simulations of large numbers of cells; it enables several rounds of infection by an evolving pathogen; and, it models building of memory. In addition, we also suggest a set of metrics for comparing clonal trees and measuring their properties. Our results show that while maximum likelihood phylogenetic reconstruction methods can fail to capture key features of clonal tree expansion if applied naively, a simple post-processing of their results, where short branches are contracted, leads to inferences that are better than alternative methods.

General Theory of Specific Binding: Insights from a Genetic-Mechano-Chemical Protein Model

Proteins need to selectively interact with specific targets among a multitude of similar molecules in the cell. However, despite a firm physical understanding of binding interactions, we lack a general theory of how proteins evolve high specificity. Here, we present such a model that combines chemistry, mechanics, and genetics and explains how their interplay governs the evolution of specific protein-ligand interactions. The model shows that there are many routes to achieving molecular discrimination-by varying degrees of flexibility and shape/chemistry complementarity-but the key ingredient is precision. Harder discrimination tasks require more collective and precise coaction of structure, forces, and movements. Proteins can achieve this through correlated mutations extending far from a binding site, which fine-tune the localized interaction with the ligand. Thus, the solution of more complicated tasks is enabled by increasing the protein size, and proteins become more evolvable and robust when they are larger than the bare minimum required for discrimination. The model makes testable, specific predictions about the role of flexibility and shape mismatch in discrimination, and how evolution can independently tune affinity and specificity. Thus, the proposed theory of specific binding addresses the natural question of "why are proteins so big?". A possible answer is that molecular discrimination is often a hard task best performed by adding more layers to the protein.

Moving the needle: Employing deep reinforcement learning to push the boundaries of coarse-grained vaccine models

Highly mutable infectious disease pathogens (hm-IDPs) such as HIV and influenza evolve faster than the human immune system can contain them, allowing them to circumvent traditional vaccination approaches and causing over one million deaths annually. Agent-based models can be used to simulate the complex interactions that occur between immune cells and hm-IDP-like proteins (antigens) during affinity maturation-the process by which antibodies evolve. Compared to existing experimental approaches, agent-based models offer a safe, low-cost, and rapid route to study the immune response to vaccines spanning a wide range of design variables. However, the highly stochastic nature of affinity maturation and vast sequence space of hm-IDPs render brute force searches intractable for exploring all pertinent vaccine design variables and the subset of immunization protocols encompassed therein. To address this challenge, we employed deep reinforcement learning to drive a recently developed agent-based model of affinity maturation to focus sampling on immunization protocols with greater potential to improve the chosen metrics of protection, namely the broadly neutralizing antibody (bnAb) titers or fraction of bnAbs produced. Using this approach, we were able to coarse-grain a wide range of vaccine design variables and explore the relevant design space. Our work offers new testable insights into how vaccines should be formulated to maximize protective immune responses to hm-IDPs and how they can be minimally tailored to account for major sources of heterogeneity in human immune responses and various socioeconomic factors. Our results indicate that the first 3 to 5 immunizations, depending on the metric of protection, should be specially tailored to achieve a robust protective immune response, but that beyond this point further immunizations require only subtle changes in formulation to sustain a durable bnAb response.

On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations

The accurate and efficient calculation of protein-protein binding affinities is an essential component in antibody and antigen design and optimization, and in computer modeling of antibody affinity maturation. Such calculations remain challenging despite advances in computer hardware and algorithms, primarily because proteins are flexible molecules, and thus, require explicit or implicit incorporation of multiple conformational states into the computational procedure. The astronomical size of the amino acid sequence space further compounds the challenge by requiring predictions to be computed within a short time so that many sequence variants can be tested. In this study, we compare three classes of methods for antibody/antigen (Ab/Ag) binding affinity calculations: (i) a method that relies on the physical separation of the Ab/Ag complex in equilibrium molecular dynamics (MD) simulations, (ii) a collection of 18 scoring functions that act on an ensemble of structures created using homology modeling software, and (iii) methods based on the molecular mechanics-generalized Born surface area (MM-GBSA) energy decomposition, in which the individual contributions of the energy terms are scaled to optimize agreement with the experiment. When applied to a set of 49 antibody mutations in two Ab/HIV gp120 complexes, all of the methods are found to have modest accuracy, with the highest Pearson correlations reaching about 0.6. In particular, the most computationally intensive method, i.e., MD simulation, did not outperform several scoring functions. The optimized energy decomposition methods provided marginally higher accuracy, but at the expense of requiring experimental data for parametrization. Within each method class, we examined the effect of the number of independent computational replicates, i.e., modeled structures or reinitialized MD simulations, on the prediction accuracy. We suggest using about ten modeled structures for scoring methods, and about five simulation replicates for MD simulations as a rule of thumb for obtaining reasonable convergence. We anticipate that our study will be a useful resource for practitioners working to incorporate binding affinity calculations within their protein design and optimization process.

Investigation of the binding and dynamic features of A.30 variant revealed higher binding of RBD for hACE2 and escapes the neutralizing antibody: A molecular simulation approach

With the emergence of Delta and Omicron variants, many other important variants of SARS-CoV-2, which cause Coronavirus disease-2019, including A.30, are reported to increase the concern created by the global pandemic. The A.30 variant, reported in Tanzania and other countries, harbors spike gene mutations that help this strain to bind more robustly and to escape neutralizing antibodies. The present study uses molecular modelling and simulation-based approaches to investigate the key features of this strain that result in greater infectivity. The protein-protein docking results for the spike protein demonstrated that additional interactions, particularly two salt-bridges formed by the mutated residue Lys484, increase binding affinity, while the loss of key residues at the N terminal domain (NTD) result in a change to binding conformation with monoclonal antibodies, thus escaping their neutralizing effects. Moreover, we deeply studied the atomic features of these binding complexes through molecular simulation, which revealed differential dynamics when compared to wild type. Analysis of the binding free energy using MM/GBSA revealed that the total binding free energy (TBE) for the wild type receptor-binding domain (RBD) complex was -58.25 kcal/mol in contrast to the A.30 RBD complex, which reported -65.59 kcal/mol. The higher TBE for the A.30 RBD complex signifies a more robust interaction between A.30 variant RBD with ACE2 than the wild type, allowing the variant to bind and spread more promptly. The BFE for the wild type NTD complex was calculated to be -65.76 kcal/mol, while the A.30 NTD complex was estimated to be -49.35 kcal/mol. This shows the impact of the reported substitutions and deletions in the NTD of A.30 variant, which consequently reduce the binding of mAb, allowing it to evade the immune response of the host. The reported results will aid the development of cross-protective drugs against SARS-CoV-2 and its variants.

A Deep Learning Model for Accurate Diagnosis of Infection Using Antibody Repertoires

The adaptive immune receptor repertoire consists of the entire set of an individual's BCRs and TCRs and is believed to contain a record of prior immune responses and the potential for future immunity. Analyses of TCR repertoires via deep learning (DL) methods have successfully diagnosed cancers and infectious diseases, including coronavirus disease 2019. However, few studies have used DL to analyze BCR repertoires. In this study, we collected IgG H chain Ab repertoires from 276 healthy control subjects and 326 patients with various infections. We then extracted a comprehensive feature set consisting of 10 subsets of repertoire-level features and 160 sequence-level features and tested whether these features can distinguish between infected individuals and healthy control subjects. Finally, we developed an ensemble DL model, namely, DL method for infection diagnosis (https://github.com/chenyuan0510/DeepID), and used this model to differentiate between the infected and healthy individuals. Four subsets of repertoire-level features and four sequence-level features were selected because of their excellent predictive performance. The DL method for infection diagnosis outperformed traditional machine learning methods in distinguishing between healthy and infected samples (area under the curve = 0.9883) and achieved a multiclassification accuracy of 0.9104. We also observed differences between the healthy and infected groups in V genes usage, clonal expansion, the complexity of reads within clone, the physical properties in the α region, and the local flexibility of the CDR3 amino acid sequence. Our results suggest that the Ab repertoire is a promising biomarker for the diagnosis of various infections.

Conformational Entropy as a Potential Liability of Computationally Designed Antibodies

In silico antibody discovery is emerging as a viable alternative to traditional in vivo and in vitro approaches. Many challenges, however, remain open to enabling the properties of designed antibodies to match those produced by the immune system. A major question concerns the structural features of computer-designed complementarity determining regions (CDRs), including the role of conformational entropy in determining the stability and binding affinity of the designed antibodies. To address this problem, we used enhanced-sampling molecular dynamics simulations to compare the free energy landscapes of single-domain antibodies (sdAbs) designed using structure-based (DesAb-HSA-D3) and sequence-based approaches (DesAbO), with that of a nanobody derived from llama immunization (Nb10). Our results indicate that the CDR3 of DesAbO is more conformationally heterogeneous than those of both DesAb-HSA-D3 and Nb10, and the CDR3 of DesAb-HSA-D3 is slightly more dynamic than that of Nb10, which is the original scaffold used for the design of DesAb-HSA-D3. These differences underline the challenges in the rational design of antibodies by revealing the presence of conformational substates likely to have different binding properties and to generate a high entropic cost upon binding.

Complementary Roles of Antibody Heavy and Light Chain Somatic Hypermutation in Conferring Breadth and Potency to the HIV-1-Specific CAP256-VRC26 bNAb Lineage

Some HIV-infected people develop broadly neutralizing antibodies (bNAbs) that block many diverse, unrelated strains of HIV from infecting target cells and, through passive immunization, protect animals and humans from infection. Therefore, understanding the development of bNAbs and their neutralization can inform the design of an HIV vaccine. Here, we extend our previous studies of the ontogeny of the CAP256-VRC26 V2-targeting bNAb lineage by defining the mutations that confer neutralization to the unmutated common ancestor (CAP256.UCA). Analysis of the sequence of the CAP256.UCA showed that many improbable mutations were located in the third complementarity-determining region of the heavy chain (CDRH3) and the heavy chain framework 3 (FR3). Transferring the CDRH3 from bNAb CAP256.25 (63% breadth and 0.003 μg/mL potency) into the CAP256.UCA introduced breadth and the ability to neutralize emerging viral variants. In addition, we showed that the framework and light chain contributed to potency and that the second CDR of the light chain forms part of the paratope of CAP256.25. Notably, a minimally mutated CAP256 antibody, with 41% of the mutations compared to bNAb CAP256.25, was broader (64% breadth) and more potent (0.39 μg/mL geometric potency) than many unrelated bNAbs. Together, we have identified key regions and mutations that confer breadth and potency in a V2-specific bNAb lineage. These data indicate that immunogens that target affinity maturation to key sites in CAP256-VRC26-like precursors, including the CDRHs and light chain, could rapidly elicit breadth through vaccination. IMPORTANCE A major focus in the search for an HIV vaccine is elucidating the ontogeny of broadly neutralizing antibodies (bNAbs), which prevent HIV infection in vitro and in vivo. The unmutated common ancestors (UCAs) of bNAbs are generally strain specific and acquire breadth through extensive, and sometimes redundant, somatic hypermutation during affinity maturation. We investigated which mutations in the CAP256-VRC26 bNAb lineage conferred neutralization capacity to the UCA. We found that mutations in the antibody heavy and light chains had complementary roles in neutralization breadth and potency, respectively. The heavy chain, particularly the third complementarity-determining region, was responsible for conferring breadth. In addition, previously uninvestigated mutations in the framework also contributed to breadth. Together, approximately half of the mutations in CAP256.25 were necessary for broader and more potent neutralization than many unrelated neutralizing antibodies. Vaccine approaches that promote affinity maturation at key sites could therefore more rapidly produce antibodies with neutralization breadth.

Multiscale affinity maturation simulations to elicit broadly neutralizing antibodies against HIV

The design of vaccines against highly mutable pathogens, such as HIV and influenza, requires a detailed understanding of how the adaptive immune system responds to encountering multiple variant antigens (Ags). Here, we describe a multiscale model of B cell receptor (BCR) affinity maturation that employs actual BCR nucleotide sequences and treats BCR/Ag interactions in atomistic detail. We apply the model to simulate the maturation of a broadly neutralizing Ab (bnAb) against HIV. Starting from a germline precursor sequence of the VRC01 anti-HIV Ab, we simulate BCR evolution in response to different vaccination protocols and different Ags, which were previously designed by us. The simulation results provide qualitative guidelines for future vaccine design and reveal unique insights into bnAb evolution against the CD4 binding site of HIV. Our model makes possible direct comparisons of simulated BCR populations with results of deep sequencing data, which will be explored in future applications.

Vaccination has saved more lives than any other medical procedure. But, we do not have robust ways to develop vaccines against highly mutable pathogens. For example, there is no effective vaccine against HIV, and a universal vaccine against diverse strains of influenza is also not available. The development of immunization strategies to elicit antibodies that can neutralize diverse strains of highly mutable pathogens (so-called ‘broadly neutralizing antibodies’, or bnAbs) would enable the design of universal vaccines against such pathogens, as well as other viruses that may emerge in the future. In this paper, we present an agent-based model of affinity maturation–the Darwinian process by which antibodies evolve against a pathogen–that, for the first time, enables the in silico investigation of real germline nucleotide sequences of antibodies known to evolve into potent bnAbs, evolving against real amino acid sequences of HIV-based vaccine-candidate proteins. Our results provide new insights into bnAb evolution against HIV, and can be used to qualitatively guide the future design of vaccines against highly mutable pathogens.

A Coarse-Grained Model of Affinity Maturation Indicates the Importance of B-Cell Receptor Avidity in Epitope Subdominance

The elicitation of broadly neutralizing antibodies (bnAbs) is a major goal in the design of vaccines against rapidly-mutating viruses. In the case of influenza, many bnAbs that target conserved epitopes on the stem of the hemagglutinin protein (HA) have been discovered. However, these antibodies are rare, are not boosted well upon reinfection, and often have low neutralization potency, compared to strain-specific antibodies directed to the HA head. Different hypotheses have been proposed to explain this phenomenon. We use a coarse-grained computational model of the germinal center reaction to investigate how B-cell receptor binding valency affects the growth and affinity maturation of competing B-cells. We find that receptors that are unable to bind antigen bivalently, and also those that do not bind antigen cooperatively, have significantly slower rates of growth, memory B-cell production, and, under certain conditions, rates of affinity maturation. The corresponding B-cells are predicted to be outcompeted by B-cells that bind bivalently and cooperatively. We use the model to explore strategies for a universal influenza vaccine, e.g., how to boost the concentrations of the slower growing cross-reactive antibodies directed to the stem. The results suggest that, upon natural reinfections subsequent to vaccination, the protectiveness of such vaccines would erode, possibly requiring regular boosts. Collectively, our results strongly support the importance of bivalent antibody binding in immunodominance, and suggest guidelines for developing a universal influenza vaccine.

The Omicron (B.1.1.529) variant of SARS-CoV-2 binds to the hACE2 receptor more strongly and escapes the antibody response: Insights from structural and simulation data

As SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) continues to inflict chaos globally, a new variant officially known as B.1.1.529 was reported in South Africa and was found to harbor 30 mutations in the spike protein. It is too early to speculate on transmission and hospitalizations. Hence, more analyses are required, particularly to connect the genomic patterns to the phenotypic attributes to reveal the binding differences and antibody response for this variant, which can then be used for therapeutic interventions. Given the urgency of the required analysis and data on the B.1.1.529 variant, we have performed a detailed investigation to provide an understanding of the impact of these novel mutations on the structure, function, and binding of RBD to hACE2 and mAb to the NTD of the spike protein. The differences in the binding pattern between the wild type and B.1.1.529 variant complexes revealed that the key substitutions Asn417, Ser446, Arg493, and Arg498 in the B.1.1.529 RBD caused additional interactions with hACE2 and the loss of key residues in the B.1.1.529 NTD resulted in decreased interactions with three CDR regions (1-3) in the mAb. Further investigation revealed that B.1.1.529 displayed a stable dynamic that follows a global stability trend. In addition, the dissociation constant (K_D), hydrogen bonding analysis, and binding free energy calculations further validated the findings. Hydrogen bonding analysis demonstrated that significant hydrogen bonding reprogramming took place, which revealed key differences in the binding. The total binding free energy using MM/GBSA and MM/PBSA further validated the docking results and demonstrated significant variations in the binding. This study is the first to provide a basis for the higher infectivity of the new SARS-CoV-2 variants and provides a strong impetus for the development of novel drugs against them.

Structural Basis of Antibody Conformation and Stability Modulation by Framework Somatic Hypermutation

Accumulation of somatic hypermutation (SHM) is the primary mechanism to enhance the binding affinity of antibodies to antigens in vivo. However, the structural basis of the effects of many SHMs remains elusive. Here, we integrated atomistic molecular dynamics (MD) simulation and data mining to build a high-throughput structural bioinformatics pipeline to study the effects of individual and combination SHMs on antibody conformation, flexibility, stability, and affinity. By applying this pipeline, we characterized a common mechanism of modulation of heavy-light pairing orientation by frequent SHMs at framework positions 39_H, 91_H, 38_L, and 87_L through disruption of a conserved hydrogen-bond network. Q39L_H alone and in combination with light chain framework 4 (FWR4_L) insertions further modulated the elbow angle between variable and constant domains of many antibodies, resulting in improved binding affinity for a subset of anti-HIV-1 antibodies. Q39L_H also alleviated aggregation induced by FWR4_L insertion, suggesting remote epistasis between these SHMs. Altogether, this study provides tools and insights for understanding antibody affinity maturation and for engineering functionally improved antibodies.

Computational modelling of potentially emerging SARS-CoV-2 spike protein RBDs mutations with higher binding affinity towards ACE2: A structural modelling study

The spike protein of SARS-CoV-2 and the host ACE2 receptor plays a vital role in the entry to the cell. Among which the hotspot residue 501 is continuously subjected to positive selection pressure and induces unusual virulence. Keeping in view the importance of the hot spot residue 501, we predicted the potentially emerging structural variants of 501 residue. We analyzed the binding pattern of wild type and mutants (Spike RBD) to the ACE2 receptor by deciphering variations in the amino acids' interaction networks by graph kernels along with evolutionary, network metrics, and energetic information. Our analysis revealed that N501I, N501T, and N501V increase the binding affinity and alter the intra and inter-residue bonding networks. The N501T has shown strong positive selection and fitness in other animals. Docking results and repeated simulations (three times) confirmed the structural stability and tighter binding of these three variants, correlated with the previous results following the global stability trend. Consequently, we reported three variants N501I, N501T, and N501V could worsen the situation further if they emerged. The relations between the viral fitness and binding affinity is a complicated game thus the emergence of high affinity mutations in the SARS-CoV-2 RBD brings up the question of whether or not positive selection favours these mutations or not?

Exploring the role of framework mutations in enabling breadth of a cross-reactive antibody (CR3022) against the SARS-CoV-2 RBD and its variants of concern

Cross-reactive and broadly neutralizing antibodies against surface proteins of diverse strains of rapidly evolving viral pathogens like SARS-CoV-2 can prevent infection and therefore are crucial for the development of effective universal vaccines. While antibodies typically incorporate mutations in their complementarity determining regions during affinity maturation, mutations in the framework regions have been reported as players in determining properties of broadly neutralizing antibodies against HIV and the Influenza virus. We propose an increase in the cross-reactive potential of CR3022 against the emerging SARS- CoV-2 variants of concern through enhanced conformational flexibility. In this study, we use molecular dynamics simulations, in silico mutagenesis, structural modeling, and docking to explore the role of light chain FWR mutations in CR3022, a SARS-CoV anti-spike (S)-protein antibody cross-reactive to the S-protein receptor binding domain of SARS-CoV-2. Our study shows that single substitutions in the light chain framework region of CR3022 with conserved epitopes across SARS-CoV strains allow targeting of diverse antibody epitope footprints that align with the epitopes of recently-categorized neutralizing antibody classes while enabling binding to more than one strain of SARS-CoV-2. Our study has implications for rapid and evolution-based engineering of broadly neutralizing antibodies and reaffirms the role of framework mutations in effective change of antibody orientation and conformation via improved flexibility.Communicated by Ramaswamy H. Sarma.

Structural-Dynamics and Binding Analysis of RBD from SARS-CoV-2 Variants of Concern (VOCs) and GRP78 Receptor Revealed Basis for Higher Infectivity

Glucose-regulated protein 78 (GRP78) might be a receptor for SARS-CoV-2 to bind and enter the host cell. Recently reported mutations in the spike glycoprotein unique to the receptor-binding domain (RBD) of different variants might increase the binding and pathogenesis. However, it is still not known how these mutations affect the binding of RBD to GRP78. The current study provides a structural basis for the binding of GRP78 to the different variants, i.e., B.1.1.7, B.1.351, B.1.617, and P.1 (spike RBD), of SARS-CoV-2 using a biomolecular simulation approach. Docking results showed that the new variants bound stronger than the wild-type, which was further confirmed through the free energy calculation results. All-atom simulation confirmed structural stability, which was consistent with previous results by following the global stability trend. We concluded that the increased binding affinity of the B.1.1.7, B.1.351, and P.1 variants was due to a variation in the bonding network that helped the virus induce a higher infectivity and disease severity. Consequently, we reported that the aforementioned new variants use GRP78 as an alternate receptor to enhance their seriousness.

Stress-dependent flexibility of a full-length human monoclonal antibody: Insights from molecular dynamics to support biopharmaceutical development

After several decades of advancements in drug discovery, product development of biopharmaceuticals remains a time- and resource-consuming endeavor. One of the main reasons is associated to the lack of fundamental understanding of conformational dynamics of such biologic entities, and how they respond to various stresses encountered during manufacturing. In this work, we have studied the conformational dynamics of human IgG₁κ b12 monoclonal antibody (mAb) using molecular dynamics simulations. The hundreds of nanoseconds long trajectories reveal that b12 mAb is highly flexible. Its variable domains show greater conformational fluctuations than the constant domains. Additionally, it collapses towards a more globular shape in response to thermal stress, leading to decrease in the total solvent exposed surface area and radius of gyration. This behavior is more pronounced for the deglycosylated b12 mAb, and it appears to correlate with increase in inter-domain contacts between specific regions of the antibody. Conformational fluctuations also cause temporary formation and disruption of hydrophobic and charged patches on the antibody surface, which is particularly important for the prediction of CMC properties during development phases of antibody-based biotherapeutics. The insights gained through these simulations may help the development of biologic drugs, especially with regards to manufacturing processes where antibodies may undergo significant thermal stress.

Regulatory Approved Monoclonal Antibodies Contain Framework Mutations Predicted From Human Antibody Repertoires

Monoclonal antibodies (mAbs) are an important class of therapeutics used to treat cancer, inflammation, and infectious diseases. Identifying highly developable mAb sequences in silico could greatly reduce the time and cost required for therapeutic mAb development. Here, we present position-specific scoring matrices (PSSMs) for antibody framework mutations developed using baseline human antibody repertoire sequences. Our analysis shows that human antibody repertoire-based PSSMs are consistent across individuals and demonstrate high correlations between related germlines. We show that mutations in existing therapeutic antibodies can be accurately predicted solely from baseline human antibody sequence data. We find that mAbs developed using humanized mice had more human-like FR mutations than mAbs originally developed by hybridoma technology. A quantitative assessment of entire framework regions of therapeutic antibodies revealed that there may be potential for improving the properties of existing therapeutic antibodies by incorporating additional mutations of high frequency in baseline human antibody repertoires. In addition, high frequency mutations in baseline human antibody repertoires were predicted in silico to reduce immunogenicity in therapeutic mAbs due to the removal of T cell epitopes. Several therapeutic mAbs were identified to have common, universally high-scoring framework mutations, and molecular dynamics simulations revealed the mechanistic basis for the evolutionary selection of these mutations. Our results suggest that baseline human antibody repertoires may be useful as predictive tools to guide mAb development in the future.

The signs of negative selection in IGHV framework regions are associated with worse overall survival of chronic lymphocytic leukemia patients

The mutational status of the variable region of the immunoglobulin heavy chain (IGHV) genes remains the most significant prognostic factor in chronic lymphocytic leukemia (CLL) patients. However, the groups of mutated (M) and unmutated (UM) patients are also heterogeneous, and additional markers are used for a more accurate prognosis. The aim of our work was to determine the prognostic value of the signs of antigen selection determined by BASELINe statistics in M IGHV sequences of CLL patients. Clinical data, IGHV gene configuration, TP53, NOTCH1, SF3B1 mutations were analyzed in 127 CLL patients with M IGHV sequences. The median OS of patients with negative selection in the framework regions (FWRs) of IGHV genes was 120 months compared to 202 month in other CLL patients (P = 0.016). In multivariate Cox regression analysis Binet stage C vs A + B (P < 0.0001), SF3B1 mutations (P < 0.0001), negative selection in the FWRs (HR P = 0.007), and age ≥65 years (P = 0.034) were powerful adverse prognostic factors for OS in CLL patients with M IGHV genes. These preliminary data suggest that the signs of antigen-driven selection may be used as a prognostic factor in CLL patients with M IGHV genes in combination with other markers.

The SARS-CoV-2 B.1.618 variant slightly alters the spike RBD-ACE2 binding affinity and is an antibody escaping variant: a computational structural perspective

Continuing reports of new SARS-CoV-2 variants have caused worldwide concern and created a challenging situation for clinicians. The recently reported variant B.1.618, which possesses the E484K mutation specific to the receptor-binding domain (RBD), as well as two deletions of Tyr145 and His146 at the N-terminal binding domain (NTD) of the spike protein, must be studied in depth to devise new therapeutic options. Structural variants reported in the RBD and NTD may play essential roles in the increased pathogenicity of this SARS-CoV-2 new variant. We explored the binding differences and structural-dynamic features of the B.1.618 variant using structural and biomolecular simulation approaches. Our results revealed that the E484K mutation in the RBD slightly altered the binding affinity through affecting the hydrogen bonding network. We also observed that the flexibility of three important loops in the RBD required for binding was increased, which may improve the conformational optimization and consequently binding of the new variant. Furthermore, we found that deletions of Tyr145 and His146 at the NTD reduced the binding affinity of the monoclonal antibody (mAb) 4A8, and that the hydrogen bonding network was significantly affected consequently. This data show that the new B.1.618 variant is an antibody-escaping variant with slightly altered ACE2-RBD affinity. Moreover, we provide insights into the binding and structural-dynamics changes resulting from novel mutations in the RBD and NTD. Our results suggest the need for further in vitro and in vivo studies that will facilitate the development of possible therapies for new variants such as B.1.618.

How can polyreactive antibodies conquer rapidly evolving viruses?

Broadly neutralizing antibodies against rapidly evolving viruses (e.g., HIV-1 and influenza virus), often manifest antigen-binding promiscuity. Based on a recent study, we hypothesize on the significance of antibody polyreactivity in neutralization of rapidly evolving viruses. We propose that polyreactivity contributes to toleration of viral variants and shortens the time for generating neutralizing antibodies.

Rapid generation of potent antibodies by autonomous hypermutation in yeast

The predominant approach for antibody generation remains animal immunization, which can yield exceptionally selective and potent antibody clones owing to the powerful evolutionary process of somatic hypermutation. However, animal immunization is inherently slow, not always accessible and poorly compatible with many antigens. Here, we describe 'autonomous hypermutation yeast surface display' (AHEAD), a synthetic recombinant antibody generation technology that imitates somatic hypermutation inside engineered yeast. By encoding antibody fragments on an error-prone orthogonal DNA replication system, surface-displayed antibody repertoires continuously mutate through simple cycles of yeast culturing and enrichment for antigen binding to produce high-affinity clones in as little as two weeks. We applied AHEAD to generate potent nanobodies against the SARS-CoV-2 S glycoprotein, a G-protein-coupled receptor and other targets, offering a template for streamlined antibody generation at large.

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.

Promiscuous Esterases Counterintuitively Are Less Flexible than Specific Ones

Understanding mechanisms of promiscuity is increasingly important from a fundamental and application point of view. As to enzyme structural dynamics, more promiscuous enzymes generally have been recognized to also be more flexible. However, examples for the opposite received much less attention. Here, we exploit comprehensive experimental information on the substrate promiscuity of 147 esterases tested against 96 esters together with computationally efficient rigidity analyses to understand the molecular origin of the observed promiscuity range. Unexpectedly, our data reveal that promiscuous esterases are significantly less flexible than specific ones, are significantly more thermostable, and have a significantly increased specific activity. These results may be reconciled with a model according to which structural flexibility in the case of specific esterases serves for conformational proofreading. Our results signify that an esterase sequence space can be screened by rigidity analyses for promiscuous esterases as starting points for further exploration in biotechnology and synthetic chemistry.

Mutational fitness landscapes reveal genetic and structural improvement pathways for a vaccine-elicited HIV-1 broadly neutralizing antibody

Vaccine-based elicitation of broadly neutralizing antibodies holds great promise for preventing HIV-1 transmission. However, the key biophysical markers of improved antibody recognition remain uncertain in the diverse landscape of potential antibody mutation pathways, and a more complete understanding of anti-HIV-1 fusion peptide (FP) antibody development will accelerate rational vaccine designs. Here we survey the mutational landscape of the vaccine-elicited anti-FP antibody, vFP16.02, to determine the genetic, structural, and functional features associated with antibody improvement or fitness. Using site-saturation mutagenesis and yeast display functional screening, we found that 1% of possible single mutations improved HIV-1 envelope trimer (Env) affinity, but generally comprised rare somatic hypermutations that may not arise frequently in vivo. We observed that many single mutations in the vFP16.02 Fab could enhance affinity >1,000-fold against soluble FP, although affinity improvements against the HIV-1 trimer were more measured and rare. The most potent variants enhanced affinity to both soluble FP and Env, had mutations concentrated in antibody framework regions, and achieved up to 37% neutralization breadth compared to 28% neutralization of the template antibody. Altered heavy- and light-chain interface angles and conformational dynamics, as well as reduced Fab thermal stability, were associated with improved HIV-1 neutralization breadth and potency. We also observed parallel sets of mutations that enhanced viral neutralization through similar structural mechanisms. These data provide a quantitative understanding of the mutational landscape for vaccine-elicited FP-directed broadly neutralizing antibody and demonstrate that numerous antigen-distal framework mutations can improve antibody function by enhancing affinity simultaneously toward HIV-1 Env and FP.

Design of immunogens to elicit broadly neutralizing antibodies against HIV targeting the CD4 binding site

A vaccine which is effective against the HIV virus is considered to be the best solution to the ongoing global HIV/AIDS epidemic. In the past thirty years, numerous attempts to develop an effective vaccine have been made with little or no success, due, in large part, to the high mutability of the virus. More recent studies showed that a vaccine able to elicit broadly neutralizing antibodies (bnAbs), that is, antibodies that can neutralize a high fraction of global virus variants, has promise to protect against HIV. Such a vaccine has been proposed to involve at least three separate stages: First, activate the appropriate precursor B cells; second, shepherd affinity maturation along pathways toward bnAbs; and, third, polish the Ab response to bind with high affinity to diverse HIV envelopes (Env). This final stage may require immunization with a mixture of Envs. In this paper, we set up a framework based on theory and modeling to design optimal panels of antigens to use in such a mixture. The designed antigens are characterized experimentally and are shown to be stable and to be recognized by known HIV antibodies.

Development of antibody-dependent cell cytotoxicity function in HIV-1 antibodies

A prerequisite for the design of an HIV vaccine that elicits protective antibodies is understanding the developmental pathways that result in desirable antibody features. The development of antibodies that mediate antibody-dependent cellular cytotoxicity (ADCC) is particularly relevant because such antibodies have been associated with HIV protection in humans. We reconstructed the developmental pathways of six human HIV-specific ADCC antibodies using longitudinal antibody sequencing data. Most of the inferred naive antibodies did not mediate detectable ADCC. Gain of antigen binding and ADCC function typically required mutations in complementarity determining regions of one or both chains. Enhancement of ADCC potency often required additional mutations in framework regions. Antigen binding affinity and ADCC activity were correlated, but affinity alone was not sufficient to predict ADCC potency. Thus, elicitation of broadly active ADCC antibodies may require mutations that enable high-affinity antigen recognition along with mutations that optimize factors contributing to functional ADCC activity.

Polyreactive Broadly Neutralizing B cells Are Selected to Provide Defense against Pandemic Threat Influenza Viruses

Polyreactivity is the ability of a single antibody to bind to multiple molecularly distinct antigens and is a common feature of antibodies induced upon pathogen exposure. However, little is known about the role of polyreactivity during anti-influenza virus antibody responses. By analyzing more than 500 monoclonal antibodies (mAbs) derived from B cells induced by numerous influenza virus vaccines and infections, we found mAbs targeting conserved neutralizing influenza virus hemagglutinin epitopes were polyreactive. Polyreactive mAbs were preferentially induced by novel viral exposures due to their broad viral binding breadth. Polyreactivity augmented mAb viral binding strength by increasing antibody flexibility, allowing for adaption to imperfectly conserved epitopes. Lastly, we found affinity-matured polyreactive B cells were typically derived from germline polyreactive B cells that were preferentially selected to participate in B cell responses over time. Together, our data reveal that polyreactivity is a beneficial feature of antibodies targeting conserved epitopes.

Molecular dynamics analysis of the binding of human interleukin-6 with interleukin-6 α-receptor

Human interleukin-6 (hIL-6) is a multifunctional cytokine that regulates immune and inflammatory responses in addition to metabolic and regenerative processes and cancer. hIL-6 binding to the IL-6 receptor (IL-6Rα) induces homodimerization and recruitment of the glycoprotein (gp130) to form a hexameric signaling complex. Anti-IL-6 and IL-6R antibodies are clinically approved inhibitors of IL-6 signaling pathway for treating rheumatoid arthritis and Castleman's disease, respectively. There is a potential to develop novel small molecule IL-6 antagonists derived from understanding the structural basis for IL-6/IL-6Rα interactions. Here, we combine homology modeling with extensive molecular dynamics (MD) simulations to examine the association of hIL-6 with IL-6Rα. A comparison with MD of apo hIL-6 reveals that the binding of hIL-6 to IL-6Rα induces structural and dynamic rearrangements in the AB loop region of hIL-6, disrupting intraprotein contacts and increasing the flexibility of residues 48 to 58 of the AB loop. In contrast, due to the involvement of residues 59 to 78 in forming contacts with the receptor, these residues of the AB loop are observed to rigidify in the presence of the receptor. The binary complex is primarily stabilized by two pairs of salt bridges, Arg181 (hIL-6)- Glu182 (IL-6Rα) and Arg184 (hIL-6)- Glu183 (IL-6Rα) as well as hydrophobic and aromatic stacking interactions mediated essentially by Phe residues in both proteins. An interplay of electrostatic, hydrophobic, hydrogen bonding, and aromatic stacking interactions facilitates the formation of the hIL-6/IL-6Rα complex.

Deep Mutational Scanning of SARS-CoV-2 Receptor Binding Domain Reveals Constraints on Folding and ACE2 Binding

The receptor binding domain (RBD) of the SARS-CoV-2 spike glycoprotein mediates viral attachment to ACE2 receptor and is a major determinant of host range and a dominant target of neutralizing antibodies. Here, we experimentally measure how all amino acid mutations to the RBD affect expression of folded protein and its affinity for ACE2. Most mutations are deleterious for RBD expression and ACE2 binding, and we identify constrained regions on the RBD's surface that may be desirable targets for vaccines and antibody-based therapeutics. But a substantial number of mutations are well tolerated or even enhance ACE2 binding, including at ACE2 interface residues that vary across SARS-related coronaviruses. However, we find no evidence that these ACE2-affinity-enhancing mutations have been selected in current SARS-CoV-2 pandemic isolates. We present an interactive visualization and open analysis pipeline to facilitate use of our dataset for vaccine design and functional annotation of mutations observed during viral surveillance.

A VH1-69 antibody lineage from an infected Chinese donor potently neutralizes HIV-1 by targeting the V3 glycan supersite

An oligomannose patch around the V3 base of HIV-1 envelope glycoprotein (Env) is recognized by multiple classes of broadly neutralizing antibodies (bNAbs). Here, we investigated the bNAb response to the V3 glycan supersite in an HIV-1-infected Chinese donor by Env-specific single B cell sorting, structural and functional studies, and longitudinal analysis of antibody and virus repertoires. Monoclonal antibodies 438-B11 and 438-D5 were isolated that potently neutralize HIV-1 with moderate breadth, are encoded by the VH1-69 germline gene, and have a disulfide-linked long HCDR3 loop. Crystal structures of Env-bound and unbound antibodies revealed heavy chain-mediated recognition of the glycan supersite with a unique angle of approach and a critical role of the intra-HCDR3 disulfide. The mechanism of viral escape was examined via single-genome amplification/sequencing and glycan mutations around the N332 supersite. Our findings further emphasize the V3 glycan supersite as a prominent target for Env-based vaccine design.

Conformational diversity facilitates antibody mutation trajectories and discrimination between foreign and self-antigens

Conformational diversity and self-cross-reactivity of antigens have been correlated with evasion from neutralizing antibody responses. We utilized single cell B cell sequencing, biolayer interferometry and X-ray crystallography to trace mutation selection pathways where the antibody response must resolve cross-reactivity between foreign and self-proteins bearing near-identical contact surfaces, but differing in conformational flexibility. Recurring antibody mutation trajectories mediate long-range rearrangements of framework (FW) and complementarity determining regions (CDRs) that increase binding site conformational diversity. These antibody mutations decrease affinity for self-antigen 19-fold and increase foreign affinity 67-fold, to yield a more than 1,250-fold increase in binding discrimination. These results demonstrate how conformational diversity in antigen and antibody does not act as a barrier, as previously suggested, but rather facilitates high affinity and high discrimination between foreign and self.

Optimizing immunization protocols to elicit broadly neutralizing antibodies

Natural infections and vaccination with a pathogen typically stimulate the production of potent antibodies specific for the pathogen through a Darwinian evolutionary process known as affinity maturation. Such antibodies provide protection against reinfection by the same strain of a pathogen. A highly mutable virus, like HIV or influenza, evades recognition by these strain-specific antibodies via the emergence of new mutant strains. A vaccine that elicits antibodies that can bind to many diverse strains of the virus-known as broadly neutralizing antibodies (bnAbs)-could protect against highly mutable pathogens. Despite much work, the mechanisms by which bnAbs emerge remain uncertain. Using a computational model of affinity maturation, we studied a wide variety of vaccination strategies. Our results suggest that an effective strategy to maximize bnAb evolution is through a sequential immunization protocol, wherein each new immunization optimally increases the pressure on the immune system to target conserved antigenic sites, thus conferring breadth. We describe the mechanisms underlying why sequentially driving the immune system increasingly further from steady state, in an optimal fashion, is effective. The optimal protocol allows many evolving B cells to become bnAbs via diverse evolutionary paths.

The Effects of Framework Mutations at the Variable Domain Interface on Antibody Affinity Maturation in an HIV-1 Broadly Neutralizing Antibody Lineage

Understanding affinity maturation of antibodies that can target many variants of HIV-1 is important for vaccine development. While the antigen-binding site of antibodies is known to mutate throughout the co-evolution of antibodies and viruses in infected individuals, the roles of the mutations in the antibody framework region are not well understood. Throughout affinity maturation, the CH103 broadly neutralizing antibody lineage, from an individual designated CH505, altered the orientation of one of its antibody variable domains. The change in orientation was a response to insertions in the variable loop 5 (V5) of the HIV envelope. In this study, we generated CH103 lineage antibody variants in which residues in the variable domain interface were mutated, and measured the binding to both autologous and heterologous HIV-1 envelopes. Our data show that very few mutations in an early intermediate antibody of the lineage can improve binding toward both autologous and heterologous HIV-1 envelopes. We also crystallized an antibody mutant to show that framework mutations alone can result in a shift in relative orientations of the variable domains. Taken together, our results demonstrate the functional importance of residues located outside the antigen-binding site in affinity maturation.

Artificial Intelligence Teaches Drugs to Target Proteins by Tackling the Induced Folding Problem

We explore the possibility of a deep learning (DL) platform that steers drug design to target proteins by inducing binding-competent conformations. We deal with the fact that target proteins are usually not fixed targets but structurally adapt to the ligand in ways that need to be predicted to enable pharmaceutical discovery. In contrast with protein folding predictors, the proposed DL system integrates signals for structural disorder to predict conformations in floppy regions of the target protein that rely on associations with the purposely designed drug to maintain their structural integrity. This is tantamount to solve the drug-induced folding problem within an AI-empowered drug discovery platform. Preliminary testing of the proposed DL platform reveals that it is possible to infer the induced folding ensemble from which a therapeutically targetable conformation gets selected by DL-instructed drug design.

Deep mutational scanning of SARS-CoV-2 receptor binding domain reveals constraints on folding and ACE2 binding

The receptor binding domain (RBD) of the SARS-CoV-2 spike glycoprotein mediates viral attachment to ACE2 receptor, and is a major determinant of host range and a dominant target of neutralizing antibodies. Here we experimentally measure how all amino-acid mutations to the RBD affect expression of folded protein and its affinity for ACE2. Most mutations are deleterious for RBD expression and ACE2 binding, and we identify constrained regions on the RBD's surface that may be desirable targets for vaccines and antibody-based therapeutics. But a substantial number of mutations are well tolerated or even enhance ACE2 binding, including at ACE2 interface residues that vary across SARS-related coronaviruses. However, we find no evidence that these ACE2-affinity enhancing mutations have been selected in current SARS-CoV-2 pandemic isolates. We present an interactive visualization and open analysis pipeline to facilitate use of our dataset for vaccine design and functional annotation of mutations observed during viral surveillance.