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.

Structural determinants for naturally evolving H5N1 hemagglutinin to switch its receptor specificity.

Of the factors governing human-to-human transmission of the highly pathogenic avian-adapted H5N1 virus, the most critical is the acquisition of mutations on the viral hemagglutinin (HA) to "quantitatively switch" its binding from avian to human glycan receptors. Here, we describe a structural framework that outlines a necessary set of H5 HA receptor-binding site (RBS) features required for the H5 HA to quantitatively switch its preference to human receptors. We show here that the same RBS HA mutations that lead to aerosol transmission of A/Vietnam/1203/04 and A/Indonesia/5/05 viruses, when introduced in currently circulating H5N1, do not lead to a quantitative switch in receptor preference. We demonstrate that HAs from circulating clades require as few as a single base pair mutation to quantitatively switch their binding to human receptors. The mutations identified by this study can be used to monitor the emergence of strains having human-to-human transmission potential.

Glycan receptor binding of the influenza A virus H7N9 hemagglutinin.

The advent of H7N9 in early 2013 is of concern for a number of reasons, including its capability to infect humans, the lack of clarity in the etiology of infection, and because the human population does not have pre-existing immunity to the H7 subtype. Earlier sequence analyses of H7N9 hemagglutinin (HA) point to amino acid changes that predicted human receptor binding and impinge on the antigenic characteristics of the HA. Here, we report that the H7N9 HA shows limited binding to human receptors; however, should a single amino acid mutation occur, this would result in structural changes within the receptor binding site that allow for extensive binding to human receptors present in the upper respiratory tract. Furthermore, a subset of the H7N9 HA sequences demarcating coevolving amino acids appears to be in the antigenic regions of H7, which, in turn, could impact effectiveness of the current WHO-recommended prepandemic H7 vaccines.

Orthogonal immunoassays for IgG antibodies to SARS-CoV-2 antigens reveal that immune response lasts beyond 4 mo post illness onset.

Immune response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection during the current pandemic remains a field of immense interest and active research worldwide. Although the severity of acute infection may depend on the intensity of innate and adaptive immunity, leading to higher morbidity and mortality, the longevity of IgG antibodies, including neutralizing activity to SARS-CoV-2, is viewed as a key correlate of immune protection. Amid reports and concern that there is a rapid decay of IgG antibody levels within 1 mo to 2 mo after acute infection, we set out to study the pattern and duration of IgG antibody response to various SARS-CoV-2 antigens in asymptomatic and symptomatic patients in a community setting. Herein, we show the correlation of IgG anti-spike protein S1 subunit, receptor binding domain, nucleocapsid, and virus neutralizing antibody titers with each other and with clinical features such as length and severity of COVID-19 illness. More importantly, using orthogonal measurements, we found the IgG titers to persist for more than 4 mo post symptom onset, implying that long-lasting immunity to COVID-19 from infection or vaccination might be observed, as seen with other coronaviruses such as SARS and Middle East respiratory syndrome.

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.

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

Affinity improvement of proteins, including antibodies, by computational chemistry broadly relies on physics-based energy functions coupled with refinement. However, achieving significant enhancement of binding affinity (>10-fold) remains a challenging exercise, particularly for cross-reactive antibodies. We describe here an empirical approach that captures key physicochemical features common to antigen-antibody interfaces to predict protein-protein interaction and mutations that confer increased affinity. We apply this approach to the design of affinity-enhancing mutations in 4E11, a potent cross-reactive neutralizing antibody to dengue virus (DV), without a crystal structure. Combination of predicted mutations led to a 450-fold improvement in affinity to serotype 4 of DV while preserving, or modestly increasing, affinity to serotypes 1-3 of DV. We show that increased affinity resulted in strong in vitro neutralizing activity to all four serotypes, and that the redesigned antibody has potent antiviral activity in a mouse model of DV challenge. Our findings demonstrate an empirical computational chemistry approach for improving protein-protein docking and engineering antibody affinity, which will help accelerate the development of clinically relevant antibodies.

Rapid and Scalable Production of Functional SARS-CoV-2 Virus-like Particles (VLPs) by a Stable HEK293 Cell Pool.

At times of pandemics, such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, the situation demands rapid development and production timelines of safe and effective vaccines for delivering life-saving medications quickly to patients. Typical biologics production relies on using the lengthy and arduous approach of stable single-cell clones. Here, we used an alternative approach, a stable cell pool that takes only weeks to generate compared to a stable single-cell clone that needs several months to complete. We employed the membrane, envelope, and highly immunogenic spike proteins of SARS-CoV-2 to produce virus-like particles (VLPs) using the HEK293-F cell line as a host system with an economical transfection reagent. The cell pool showed the stability of protein expression for more than one month. We demonstrated that the production of SARS-CoV-2 VLPs using this cell pool was scalable up to a stirred-tank 2 L bioreactor in fed-batch mode. The purified VLPs were properly assembled, and their size was consistent with the authentic virus. Our particles were functional as they specifically entered the cell that naturally expresses ACE-2. Notably, this work reports a practical and cost-effective manufacturing platform for scalable SARS-CoV-2 VLPs production and chromatographic purification.

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.

Prediction of the structural interface between fibroblast growth factor23 and Burosumab using alanine scanning and molecular docking.

Burosumab, an FGF23 targeting monoclonal antibody, was approved by the FDA in 2018 for use in children and adults with X-linked hypophosphatemia (or XLH). While several clinical studies have demonstrated the long-term safety and efficacy of Burosumab, the molecular basis of FGF23-Burosumab interaction which underpins its mechanism of action remains unknown. In this study, we employed molecular docking combined with alanine scanning of epitope and paratope to predict a model of FGF23-Burosumab interaction. Then, we used the model to understand the species-species cross-reactivity of Burosumab and to reverse engineer mouse FGF23 with 'back to human' mutations to bind Burosumab. Finally, we redesigned the CDRs with two mutations to engineer an affinity enhanced variant of the antibody. Our study provides insights into the FGF23-Burosumab interaction and demonstrates that alanine-scanning coupled with molecular docking can be used to optimize antibody candidates (e.g., structure-guided affinity maturation) for therapeutic use.

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.

Generation of bispecific antibodies by structure-guided redesign of IgG constant regions.

Bispecific antibodies (BsAbs) form an exciting class of bio-therapeutics owing to their multispecificity. Although numerous formats have been developed, generation of hetero-tetrameric IgG1-like BsAbs having acceptable safety and pharmacokinetics profiles from a single cell culture system remains challenging due to the heterogeneous pairing between the four chains. Herein, we employed a structure-guided approach to engineer mutations in the constant domain interfaces (CH1-CL and CH3-CH3) of heavy and κ light chains to prevent heavy-light mispairing in the antigen binding fragment (Fab) region and heavy-heavy homodimerization in the Fc region. Transient co-transfection of mammalian cells with heavy and light chains of pre-existing antibodies carrying the engineered constant domains generates BsAbs with percentage purity ranging from 78% to 85%. The engineered BsAbs demonstrate simultaneous binding of both antigens, while retaining the thermal stability, Fc-mediated effector properties and FcRn binding properties of the parental antibodies. Importantly, since the variable domains were not modified, the mutations may enable BsAb formation from antibodies belonging to different germline origins and isotypes. The rationally designed mutations reported in this work could serve as a starting point for generating optimized solutions required for large scale production.

Competitive inhibition of heparinase by persulfonated glycosaminoglycans: a tool to detect heparin contamination.

Heparin and the low molecular weight heparins are extensively used as medicinal products to prevent and treat the formation of venous and arterial thrombi. In early 2008, administration of some heparin lots was associated with the advent of severe adverse effects, indicative of an anaphylactoid-like response. Application of orthogonal analytical tools enabled detection and identification of the contaminant as oversulfated chondroitin sulfate (OSCS) was reported in our earlier report. Herein, we investigate whether enzymatic depolymerization using the bacterially derived heparinases, given the structural understanding of their substrate specificity, can be used to identify the presence of OSCS in heparin. We also extend this analysis to examine the effect of other persulfonated glycosaminoglycans (GAGs) on the action of the heparinases. We find that all persulfonated GAGs examined were effective inhibitors of heparinase I, with IC(50) values ranging from approximately 0.5-2 µg/mL. Finally, using this biochemical understanding, we develop a rapid, simple assay to assess the purity of heparin using heparinase digestion followed by size-exclusion HPLC analysis to identify and quantify digestion products. In the context of the assay, we demonstrate that less than 0.1% (w/w) of OSCS (and other persulfonated polysaccharides) can routinely be detected in heparin.

Modified recombinant human erythropoietin with potentially reduced immunogenicity.

Recombinant human erythropoietin (rHuEPO) is a biopharmaceutical drug given to patients who have a low hemoglobin related to chronic kidney disease, cancer or anemia. However, some patients repeatedly receiving rHuEPO develop anti-rHuEPO neutralizing antibodies leading to the development of pure red cell aplasia (PRCA). The immunogenic antibody response activated by rHuEPO is believed to be triggered by T-cells recognizing EPO epitopes bound to MHC molecules displayed on the cell surface of APCs. Previous studies have reported an association between the development of anti-rHuEpo-associated PRCA and the HLA-DRB1*09 gene, which is reported to be entrenched in the Thai population. In this study, we used computational design to screen for immunogenic hotspots recognized by HLA-DRB1*09, and predicted seventeen mutants having anywhere between one through four mutations that reduce affinity for the allele, without disrupting the structural integrity and bioactivity. Five out of seventeen mutants were less immunogenic in vitro while retaining similar or slightly reduced bioactivity than rHuEPO. These engineered proteins could be the potential candidates to treat patients who are rHuEpo-dependent and express the HLA-DRB1*09 allele.

The biological function of some human transcription factor binding motifs varies with position relative to the transcription start site.

A number of previous studies have predicted transcription factor binding sites (TFBSs) by exploiting the position of genomic landmarks like the transcriptional start site (TSS). The studies' methods are generally too computationally intensive for genome-scale investigation, so the full potential of 'positional regulomics' to discover TFBSs and determine their function remains unknown. Because databases often annotate the genomic landmarks in DNA sequences, the methodical exploitation of positional regulomics has become increasingly urgent. Accordingly, we examined a set of 7914 human putative promoter regions (PPRs) with a known TSS. Our methods identified 1226 eight-letter DNA words with significant positional preferences with respect to the TSS, of which only 608 of the 1226 words matched known TFBSs. Many groups of genes whose PPRs contained a common word displayed similar expression profiles and related biological functions, however. Most interestingly, our results included 78 words, each of which clustered significantly in two or three different positions relative to the TSS. Often, the gene groups corresponding to different positional clusters of the same word corresponded to diverse functions, e.g. activation or repression in different tissues. Thus, different clusters of the same word likely reflect the phenomenon of 'positional regulation', i.e. a word's regulatory function can vary with its position relative to a genomic landmark, a conclusion inaccessible to methods based purely on sequence. Further integrative analysis of words co-occurring in PPRs also yielded 24 different groups of genes, likely identifying cis-regulatory modules de novo. Whereas comparative genomics requires precise sequence alignments, positional regulomics exploits genomic landmarks to provide a 'poor man's alignment'. By exploiting the phenomenon of positional regulation, it uses position to differentiate the biological functions of subsets of TFBSs sharing a common sequence motif.

Prediction of the binding interface between monoclonal antibody m102.4 and Nipah attachment glycoprotein using structure-guided alanine scanning and computational docking.

Nipah Virus (NiV) has been designated as a priority disease with an urgent need for therapeutic development by World Health Organization. The monoclonal antibody m102.4 binds to the immunodominant NiV receptor-binding glycoprotein (GP), and potently neutralizes NiV, indicating its potential as a therapeutic agent. Although the co-crystal structure of m102.3, an m102.4 derivative, in complex with the GP of the related Hendra Virus (HeV) has been solved, the structural interaction between m102.4 and NiV is uncharacterized. Herein, we used structure-guided alanine-scanning mutagenesis to map the functional epitope and paratope residues that govern the antigen-antibody interaction. Our results revealed that the binding of m102.4 is mediated predominantly by two residues in the HCDR3 region, which is unusually small for an antibody-antigen interaction. We performed computational docking to generate a structural model of m102.4-NiV interaction. Our model indicates that m102.4 targets the common hydrophobic central cavity and a hydrophilic rim on the GP, as observed for the m102.3-HeV co-crystal, albeit with Fv orientation differences. In summary, our study provides insight into the m102.4-NiV interaction, demonstrating that structure-guided alanine-scanning and computational modeling can serve as the starting point for additional antibody reengineering (e.g. affinity maturation) to generate potential therapeutic candidates.

Promoter analysis: gene regulatory motif identification with A-GLAM.

Reliable detection of cis-regulatory elements in promoter regions is a difficult and unsolved problem in computational biology. The intricacy of transcriptional regulation in higher eukaryotes, primarily in metazoans, could be a major driving force of organismal complexity. Eukaryotic genome annotations have improved greatly due to large-scale characterization of full-length cDNAs, transcriptional start sites (TSSs), and comparative genomics. Regulatory elements are identified in promoter regions using a variety of enumerative or alignment-based methods. Here we present a survey of recent computational methods for eukaryotic promoter analysis and describe the use of an alignment-based method implemented in the A-GLAM program.

Identification of cis-regulatory elements in gene co-expression networks using A-GLAM.

Reliable identification and assignment of cis-regulatory elements in promoter regions is a challenging problem in biology. The sophistication of transcriptional regulation in higher eukaryotes, particularly in metazoans, could be an important factor contributing to their organismal complexity. Here we present an integrated approach where networks of co-expressed genes are combined with gene ontology-derived functional networks to discover clusters of genes that share both similar expression patterns and functions. Regulatory elements are identified in the promoter regions of these gene clusters using a Gibbs sampling algorithm implemented in the A-GLAM software package. Using this approach, we analyze the cell-cycle co-expression network of the yeast Saccharomyces cerevisiae, showing that this approach correctly identifies cis-regulatory elements present in clusters of co-expressed genes.

Finding sequence motifs with Bayesian models incorporating positional information: an application to transcription factor binding sites.

BACKGROUND: Biologically active sequence motifs often have positional preferences with respect to a genomic landmark. For example, many known transcription factor binding sites (TFBSs) occur within an interval [-300, 0] bases upstream of a transcription start site (TSS). Although some programs for identifying sequence motifs exploit positional information, most of them model it only implicitly and with ad hoc methods, making them unsuitable for general motif searches. RESULTS: A-GLAM, a user-friendly computer program for identifying sequence motifs, now incorporates a Bayesian model systematically combining sequence and positional information. A-GLAM's predictions with and without positional information were compared on two human TFBS datasets, each containing sequences corresponding to the interval [-2000, 0] bases upstream of a known TSS. A rigorous statistical analysis showed that positional information significantly improved the prediction of sequence motifs, and an extensive cross-validation study showed that A-GLAM's model was robust against mild misspecification of its parameters. As expected, when sequences in the datasets were successively truncated to the intervals [-1000, 0], [-500, 0] and [-250, 0], positional information aided motif prediction less and less, but never hurt it significantly. CONCLUSION: Although sequence truncation is a viable strategy when searching for biologically active motifs with a positional preference, a probabilistic model (used reasonably) generally provides a superior and more robust strategy, particularly when the sequence motifs' positional preferences are not well characterized.

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 (GluH55) 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 GluH55 using site-directed mutagenesis followed by isothermal titration calorimetry measurements.

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.