Abstract T cell receptors (TCRs) can bind to peptides presented by MHC molecules (pMHC) as a first step to trigger a T cell response. Reliable approaches to predict TCR:pMHC binding would have broad applications in clinical diagnostics, therapeutics, and the fundamental understanding of molecular interactions. IMMREP is a community organized series of prediction contests that asks participants to predict TCR:pMHC binding on unpublished datasets. Previous iterations in 2022 and 2023 showed multiple approaches can predict TCR-pMHC binding with significant accuracy (median AUC_0.1≥0.7) for peptides where experimental data is available (“seen” peptides). In contrast, models did not outperform random guessing for peptides that have no such data available (“unseen” peptides). Here we report on the results of IMMREP25, which focused solely on unseen peptides in order to evaluate the cutting edge of the field. We received 126 named submissions predicting the specificity of 1,000 TCRs against twenty unseen peptides restricted by one of two MHC molecules (HLA-A*02:01 and HLA-B*40:01). The best performing methods showed a macro-AUC_0.1 of 0.60, significantly better than random, demonstrating significant advances in the field. The top performing methods incorporated structural modeling into their approach, indicating that especially for ‘unseen’ peptides, a structural understanding aids in the prediction of TCR:pMHC interactions. The results from this benchmark highlight the significant challenges remaining for TCR:pMHC predictions and will inform future method development.

Supplementary Fig. S1: Patient level heatmaps by assay. Supplementary Fig. S2: Overview of modalities in database. Supplementary Fig. S3: Computational pipeline for single cell RNA-seq data processing. Supplementary Fig. S4: Quality control metrics of tissue single cell data set. Supplementary Fig. S5: Cell type clusters in single-cell PBMC dataset. Supplementary Fig. S6: Myeloid cells in Merkel cell carcinoma. Supplementary Fig. S7: Response to ICB is associated with tumor cell upregulation of an interferon-γ signature. Supplementary Fig. S8: GSEA on tumor cells controlling for proliferation. Supplementary Fig. S9: Dot plot of RNA markers in CD8 clusters (tissue). Supplementary Fig. S10: Clonotype distribution between CD8 T cell lineages. Supplementary Fig. S11: CD8 lineage characterization. Supplementary Fig. S12: Clonality and neoantigen metrics of metrics of CD8 T cells. Supplementary Fig. S13: Comparisons of cell type signatures and cell proportions separated by sample origin. Supplementary Fig. S14: Epitope screening results. Supplementary Fig. S15: Public epitopes. Supplementary Fig. S16: Expression of naïve-like markers on Vδ1 T cells. Supplementary Fig. S17: GeoMx quality control for regions of interest. Supplementary Fig. S18: GeoMx image and differential expression analysis. Supplementary Fig. S19: CosMx quality control for fields of view. Supplementary Fig. S20: CosMx degree of co-localization sensitivity test shows B cell, T cell, pDC, and DC interactions. Supplementary Fig. S21: Pre-post genes. Supplementary Fig. S22: MIXCR analysis of pre-post ICB clonal dynamics. Supplementary Fig. S23: Single cell analysis of pre-post ICB clonal dynamics. Supplementary Fig. S24: Functional convergence of CD8 and γT cells. Supplementary Fig. S25: Graphical model of immune checkpoint response in MCC.

Orthomarburgviruses are the etiological agents of human Marburg virus disease, with case fatality rates reaching up to 90%. Of the limited known targets for protective antibodies on the orthomarburgvirus surface glycoprotein (GP), the GP2 wing region is undefined structurally, and its recognition by antibodies is poorly understood. We report a comprehensive genetic and structural analysis of four independent broadly reactive antibody lineages that target a common epitope within the GP2 wing, including macaque antibodies CM10, CM11.1, and CM12.1 and murine antibody 30G4. Co-crystal structures reveal the recognition of conserved structural determinants within the wing by all four antibodies. Immunogenetic analyses uncover common antibody sequence signatures that underlie interactions with the wing, leading in some cases to nearly identical modes of antibody binding. Our study advances the structural understanding of the GP2 wing protective region and defines conserved immunogenetic and structural features for broad antibody recognition, informing the development of vaccines and antibody countermeasures.

The development of an effective prophylactic hepatitis C virus (HCV) vaccine is a priority to achieve global elimination of the virus. Accurate assessment of the neutralizing breadth of antibodies induced by vaccines and a clear understanding of the antigenic differences between viral variants included in vaccines are both critical for vaccine development. Prior studies have indicated that HCV genotypes (gts) do not dictate the sensitivity of HCV envelope glycoprotein (E1E2) variants to neutralizing antibodies. However, most of these prior studies under-sampled variants from gts 2-6. Here, we selected a genetically diverse and representative panel of gt 2-6 E1E2 variants, used them to generate HCV pseudoparticles (HCVpp), and measured neutralization of these HCVpp by neutralizing antibodies and HCV-immune plasma from persons infected with gt 1-6 viruses. We found that neutralization results obtained with this gt 2-6 panel were remarkably similar to results obtained with a previously described, antigenically diverse, gt 1-predominant reference panel of 15 HCVpp. These data confirm that, even considering genetically diverse HCV variants across gt 1-6, E1E2 antigenicity is not dictated by gt, and that the previously published panel of 15 HCVpp represents neutralization of all HCV gts with reasonable accuracy.

GSDMA, the primary member of the gasdermin family found in the skin, is critical for pathogen-induced pyroptosis during infection. Recent studies revealed that another gasdermin, GSDMD, undergoes palmitoylation during pyroptosis. However, whether and how the other gasdermin members undergo lipid modification remain poorly understood. Here, we demonstrate that GSDMA is S-acylated at the conserved cysteine residues in its N-terminal domain. We show that the S-acylation of GSDMA promotes pyroptosis by facilitating its membrane anchoring and protein oligomerization, a mechanism distinct from the palmitoylation of GSDMD at the N-terminal C191 residue. In addition, we present evidence that recombinant proteins of GSDMA and GSDMD can undergo S-acylation in vitro independent of palmitoyl transferases via direct interaction with palmitoyl-CoA. Furthermore, we identify ABHD17A as one of the deacylating enzymes that regulate the dynamic fatty acylation cycle of GSDMA. Overall, our studies reveal new molecular mechanisms underlying GSDMA function through S-acylation and underscore its important role in regulating pyroptosis mediated by GSDMA.

Many proteins are known to adopt multiple distinct folded states which are often associated with key functional behavior. A predictive understanding of the properties of such fold-switching or metamorphic proteins can provide insights into protein dynamics and energetics, and enable the design of complex protein functions and molecular machines. Recently developed deep learning modeling tools, including AlphaFold, have led to dramatic increases in accuracy for prediction of protein structures from sequence, but their performance for prediction of point mutant effects or fold switching is unclear. Here we present a systematic NMR-characterized dataset of mutants of the GA/GB model fold-switching system and use it to evaluate whether current structure prediction and design methods can predict mutation-induced changes in fold state. We measured fold-state populations for variants at three key positions that differentially stabilize the 3α, 4β+α, mixed, or unfolded states, generating a quantitative experimental benchmark for mutation-level fold switching. Using this benchmark to assess and compare a panel of deep learning and physics-based modeling and design algorithms, we found that this benchmark revealed variable and position-dependent performance across methods, with certain AlphaFold2-based algorithms were able to predict mutant effects at individual sites, indicating some understanding of physical effects of residue substitutions. Additional comparisons of predictions with experimentally measured stability changes further highlighted position-dependent success and general challenges for predictive algorithms. Together, this study provides a new benchmark for mutation-induced fold switching and reveals the current capabilities and limitations of deep learning models for predicting mutation-dependent protein conformational states.

Determining the structural basis of antigen recognition by antibodies and T cell receptors (TCRs) provides critical insights into effective immune targeting and can inform design of biotherapeutics and vaccines. Accurate computational modeling of antibodies and TCRs in complex with their targets poses a major challenge for predictive methods, including AlphaFold, which is generally accurate for modeling protein complexes but has shown limited success for immune recognition. In this study we assessed the performance of AlphaFold2, AlphaFold3, increased sampling protocols, and related deep learning methods for modeling antibody-protein, antibody-peptide, and TCR-peptide-major histocompatibility complex (pMHC) recognition. We show that increased sampling and AlphaFold3 generally improve performance relative to default sampling and AlphaFold2, however predictive accuracy and improvement levels varied considerably among interface classes, with antibody-peptide complexes representing a challenge despite their small antigen size. Comparing per-case success across methods showed some complementarity, indicating opportunities for increased success through model pooling approaches, for instance increasing antibody-peptide near-native success from 41% to 59%. Analysis of AlphaFold confidence scores and modeling of a noncanonical complex provided further insights into predictive performance. These results highlight considerations for predictive antibody and TCR complex modeling efforts, while revealing key distinctions among protocols, scoring, and immune complex classes.

Supplementary Table S1. Patient cohort: Clinical data and assays Supplementary Table S2. Sample cohort: Clinical data and assays Supplementary Table S3. Pre-ICB bulk RNA-seq cohort, 63 samples Supplementary Table S4. Pre-Post ICB matched bulk RNA-seq, 28 samples, 14 patient pairs Supplementary Table S5. Number of reads for each bulk RNA-seq sample (85 samples) Supplementary Table S6. Tissue single-cell RNA-seq quality control and sample metadata (54 samples) Supplementary Table S7. Blood single-cell RNA-seq quality control and sample metadata (55 samples) Supplementary Table S8. Multiplex immunofluorescence sample cohort (44 samples) Supplementary Table S9. GeoMx sample cohort (15 samples) Supplementary Table S10. CosMx sample cohort (13 samples) Supplementary Table S11: Genes differentially expressed between MCC that respond to anti-PD-1/PD-L1 therapy (n=31) and MCC that do not respond to anti-PD-1/PD-L1 therapy (n=32) in the pre-ICB cohort (n=63). Supplementary Table S12. Enrichr results of differentially expressed genes (FDR < 0.1) in responders and non-responders prior to immunotherapy in the bulk RNA-seq dataset Supplementary Table S13. Wilcoxon markers of single-cell RNA-seq clusters for all cells in the tissue dataset Supplementary Table S14. Pseudobulk markers of tumor cells comparing immunotherapy responders to non-responders Supplementary Table S15. GSEA results of differentially expressed genes from responders and non-responders in tumor cells pseudobulk dataset. Supplementary Table S16. SCENIC tumor markers of response and non-response to immunotherapy Supplementary Table S17. Genes in single-cell derived gene signatures Supplementary Table S18. Average antibody derived tag expression for cells in CD8 T cell clusters Supplementary Table S19. Wilcoxon markers of Tcirc and Trm CD8 T cells Supplementary Table S20. Wilcoxon markers of CD8 T cell clusters Supplementary Table S21. TCR clonotype linkage of CD8 T cell clusters Supplementary Table S22. Gini index of each CD8 cluster split by sample Supplementary Table S23. Small T and large T antigens peptide pools for TCR epitope screen Supplementary Table S24. GLIPH2 results of CD8 TCRs Supplementary Table S25. CD8 T cells with TCRs that match public databases with known antigens (VDJdb, McPAS-TCR, TRAdb) Supplementary Table S26. Wilcoxon RNA markers of each cell type, split by δ chain, in the γδ/NK cell subset. Supplementary Table S27. Wilcoxon antibody derived tag markers of each cell type, split by δ chain and cellular source, in the γδ/NK cell subset. Supplementary Table S28. Wilcoxon antibody derived tag markers of δ1 versus δ2 T cells from the tissue dataset Supplementary Table S29. Genes with significant non-linear fits along Vδ1 pseudotime Supplementary Table S30. GeoMx: Differentially expressed genes between responders and non-responders in tumor regions of interest Supplementary Table S31. GeoMx: Differentially expressed genes between responders and non-responders in stroma regions of interest Supplementary Table S32. CosMx: Differentially expressed genes among cell types identified by InSituType. Supplementary Table S33. CosMx: SpatialTime matrices of degree of co-localization between cell types Supplementary Table S34. CellChat receptor ligand interaction with CD8 exhausted T cells (Tex) as targets Supplementary Table S35. Differential expressed genes between bulk RNA-seq samples pre/post immunotherapy treatment split between responders (n=7) and non-responders (n=7) Supplementary Table S36. Enrichr results of differentially expressed genes (FDR <0.1) post-immunotherapy responders in the pre/post matched bulk RNA-seq dataset

Developing a highly effective malaria vaccine remains challenging due to Plasmodium falciparum's antigenic diversity and human leukocyte antigen (HLA) polymorphisms, which complicate antigen selection and limit immune protection. The first recommended malaria vaccine, RTS,S, provides partial, allele-specific protection with waning immunity, and recently developed R21 vaccine will likely encounter the same hurdles. To address these challenges, we developed a computational decision-support framework that integrates P. falciparum sequence diversity, predicted T cell epitope-HLA binding, and population-specific HLA allele frequencies from sub-Saharan Africa to prioritize conserved T cell epitopes for experimental evaluation. We analyzed 42 P. falciparum proteins, previously identified as vaccine candidates, generated consensus sequences from 18 African countries, and incorporated HLA allele frequencies from 24 sub-Saharan populations. CD8+ and CD4+ T cell epitopes were predicted using NetMHCpan-4.1 and NetMHCIIpan-4.1. Our tool, T cell Epitope Nomination (TEpiNom), applies integer linear programming to prioritize epitopes based on sequence conservation (>95%), predicted HLA binding affinity (median rank <10%), and breadth of HLA locus coverage, while minimizing redundancy across antigen targets. Using this framework, we identified 2265 MHC I and 1992 MHC II conserved epitopes spanning pre-erythrocytic, erythrocytic, and sexual stage proteins. Prioritized MHC I epitopes from pre-erythrocytic antigens FabZ, FabG, p36, and PKG achieved 100% predicted inter-locus MHC I coverage, and MHC II epitopes from pre-erythrocytic, erythrocytic, or sexual antigens provided 100% coverage for a given parasite life stage. In parallel, our search for epitope-dense regions identified short, conserved protein segments across all parasite life stages that independently provided complete predicted inter-locus coverage, highlighting compact targets with high HLA-promiscuous potential. Together, this study presents TEpiNom for the systematic prioritization of T cell epitopes and epitope-dense regions, to streamline preclinical malaria vaccine development by refining computational predictions into experimentally tractable candidates. The framework is adaptable for vaccine development against other diverse and evasive pathogens.

T cell receptors (TCRs) specific for cancer neoantigens are important for anti-tumor immunity and immunotherapy. To understand the structural basis for T cell recognition of cancer neoantigens, we studied oligoclonal TCRs from patients with melanoma that recognize a neoepitope arising from a driver mutation in NRAS (NRASQ61K) presented by HLA-A1. Structures of these TCRs in unbound form and bound to NRASQ61K-HLA-A1 revealed that they employ chemically distinct strategies and engagement modes to distinguish between mutant and wild-type NRAS. The structures explain how the NRASQ61K mutation rendered a self-antigen visible to T cells. We additionally benchmarked AlphaFold-based modeling of these complexes, showing that predictive accuracy varies markedly across TCR-peptide-MHC targets. We found that conformational plasticity can dramatically impact complex assembly accuracy. These findings define the basis for TCR recognition of a cancer neoantigen and provide stringent tests for computational modeling of TCR-peptide-MHC interactions relevant to cancer immunotherapy.

Development of an effective HCV vaccine requires the induction of both broadly neutralizing antibodies (bnAbs) and a robust cellular response. One issue that has arisen is that HCV subunit vaccines have limited immunogenicity, thus requiring multivalent formats in order to elicit a robust anti-HCV immune response. Toward that end, nanoparticle vaccines possess the ability to facilitate a controlled multivalent presentation and trafficking to lymph nodes, where they can interact with both arms of the immune system. Here, we used a soluble, secreted form of E1E2 (sE1E2) to assemble native E1E2 into a nanoparticle platform using a post-purification coupling assembly system. Nanoparticles were assembled by purifying sE1E2 containing a C-terminal SpyTag and an mi3-SpyCatcher fusion separately and covalently coupling the components via incubation. Free sE1E2-SpyTag was removed from nanoparticle preparations via gel filtration. The sE1E2-mi3 nanoparticles are fully competent to bind conformation-dependent bnAbs, indicating retention of a native assembly in the nanoparticle format. Electron microscopy analysis showed a clear incorporation of sE1E2 on the surface of the nanoparticle. Immunogenicity of sE1E2-mi3 nanoparticles was examined relative to sE1E2 alone and membrane-bound E1E2 (mbE1E2) following inoculation of groups of CD1 mice. Assessment of the immunogenicity of the sE1E2-mi3 nanoparticles showed that the nanoparticle assembly has a similar immunogenicity profile to that of mbE1E2 after only a prime and one boost, and overall superior to sE1E2. This proof-of-principle study sets the stage for further exploration of nanoparticles and other multivalent platforms for the development of E1E2-based vaccines. Importance:Hepatitis C virus infects approximately 50 million people, and at present no effective HCV vaccine exists. Due to the high sequence variability of HCV and the resulting difficulty in developing a vaccine that elicits a broadly neutralizing response, multiple efforts are underway to enhance the immunogenicity of HCV vaccine candidates. In this study, we incorporated native soluble, secreted E1E2 (sE1E2) into a 60-mer nanoparticle via the SpyTag-SpyCatcher system and covalent isopeptide bond attachment using the purified components. These nanoparticles are antigenically intact and elicit a neutralizing antibody response at an earlier time point in the immunization regimen than the corresponding subunit vaccine. These studies show that a well-characterized sE1E2 platform compatible with multiple genotypes can be coupled to nanoparticles for use as a vaccine candidate.

Models of Ab-antigen complexes can be used to understand interaction mechanisms and for improving affinity. This study evaluates the use of the protein structure prediction algorithm AlphaFold (AF) for exploration of interactions between peptide epitope tags and the smallest functional antibody fragments, nanobodies (Nbs). Although past studies of AF for modeling antibody-target (antigen) interactions suggested modest algorithm performance, those were primarily focused on Ab-protein interactions, while the performance and utility of AF for Nb-peptide interactions, which are generally less complex due to smaller antigens, smaller binding domains, and fewer chains, is less clear. In this study we evaluated the performance of AF for predicting the structures of Nbs bound to experimentally validated, linear, short peptide epitopes (Nb-tag pairs). We expanded the pool of experimental data available for comparison through crystallization and structural determination of a previously reported Nb-tag complex (Nb127). Models of Nb-tag pair structures generated from AF were variable with respect to consistency with experimental data, with good performance in just over half (4 out of 6) of cases. Even among Nb-tag pairs successfully modeled in isolation, efforts to translate modeling to more complex contexts failed, suggesting an underappreciated role of the size and complexity of inputs in AF modeling success. Finally, the model of a Nb-tag pair with minimal previous characterization was used to guide the design of a peptide-electrophile conjugate that undergoes covalent crosslinking with Nb upon binding. These findings highlight the utility of minimized antibody and antigen structures to maximize insights from AF modeling.

Developing a highly effective malaria vaccine remains challenging due to Plasmodium falciparum's antigenic diversity and human leukocyte antigen (HLA) polymorphisms, which complicate vaccine antigen selection and limit immune protection. The first recommended malaria vaccine, RTS,S, provides only partial, allele-specific protection with waning immunity over time, and the more recently developed R21 vaccine will likely encounter the same hurdles. To address these challenges, we developed a computational tool that integrates P. falciparum sequence diversity, predicted T cell epitope-HLA binding affinities, and HLA allele frequencies from sub-Saharan Africa to identify conserved, immunogenic epitopes with broad population coverage. We analyzed 42 P. falciparum proteins, previously identified as vaccine candidate antigens, and generated consensus sequences using data from 18 African countries, and then incorporated HLA allele frequencies from 24 sub-Saharan populations. CD8+ and CD4+ T cell epitopes were predicted using NetMHCpan-4.1 and NetMHCIIpan-4.1. Our novel tool, T cell Epitope Nomination (TEpiNom), used greedy optimization to filter and select epitopes based on epitope sequence conservation (>95%), binding affinity (median rank <10%), and broad HLA coverage, minimizing redundancy to reduce immune escape risk. Our tool identified 2,265 MHC I and 1,992 MHC II conserved epitopes spanning pre-erythrocytic, erythrocytic, and sexual stage proteins. Key MHC I epitopes from pre-erythrocytic antigens HSP70-2, SLARP/SAP1, p36, FabZ, LISP1, LSA1, UIS3, p24_2, PL, and FabG achieved near 100% HLA-A, HLA-B, and HLA-C coverage, and MHC II epitopes from pre-erythrocytic, erythrocytic, or sexual antigens provided 98.5%-100% coverage for a given parasite life stage. This strategy advances malaria vaccine design by integrating epitope promiscuity and multistage antigen selection to support broad, durable protection and identify promising multi-epitope malaria vaccine candidates for subsequent experimental validation. Our computational framework is adaptable for vaccine development against other genetically diverse and immunologically evasive pathogens.

T cells play a crucial role in clearing SARS-CoV-2 and in forming long-term memory responses to that coronavirus. The highly immunogenic nucleocapsid (N) protein of SARS-CoV-2 is much more conserved than the spike (S) protein across variants of concern, making it an attractive vaccine target for activating cytotoxic CD8+ T cells. Of particular interest are the immunodominant N epitopes LLL and SPR. Whereas LLL elicits a clonally restricted T cell response, the response to SPR is highly diverse. To understand the basis for this difference, here we determine structures of T cell receptors (TCRs) bound to LLL–HLA-A2 and SPR–HLA-B7, revealing the structural underpinnings of highly restricted Vα gene usage by LLL-specific TCRs, as well as multiple structural solutions to recognizing SPR and thereby generating a clonally diverse T cell response to that epitope. These structures also provide frameworks for understanding T cell recognition of SARS-CoV-2 variants and other coronaviruses. Finally, we compare the X-ray structures of TCR–LLL–HLA-A2 and TCR–SPR–HLA-B7 complexes with models predicted by multiple versions of AlphaFold, highlighting some success while showing room for improvement. Overall, our findings expand understanding of coronavirus T cell recognition, informing vaccine design and advances in computational modeling approaches. Previous structural studies of T cell recognition of SARS-CoV-2 have been confined to spike epitopes. Here the authors assess T cell recognition of SARS-CoV-2 nucleocapsid epitopes, which are more conserved than spike epitopes, providing structural insights into recognition of two epitopes.

Broadly neutralizing antibodies (bnAbs) against HIV hold promise as therapeutic and prophylactic agents, but realizing this potential requires antibodies that function across the antigenic heterogeneity of the HIV envelope glycoprotein (Env). Although numerous bnAbs have been isolated from infected individuals, their breadth and potency may not be sufficient to tackle global viral diversity, motivating efforts to further improve their neutralization capacity. Here, we address this challenge using a multistate antibody engineering approach integrating deep mutational scanning with combinatorial library screening across diverse Env variants. This strategy enables identification of mutation patterns that confer improved binding across antigenically distinct targets. Starting from one of the best-in-class CD4-binding site bnAbs, we performed iterative optimization to increase binding affinity across diverse Env variants. The resulting lead candidate exhibited improved breadth and up to 100-fold higher potency against pseudoviruses from large cross-clade historical and contemporary panels while maintaining biophysical and pharmacokinetic profiles conducive to clinical development. Structural and molecular dynamics analyses revealed a unique tri-tyrosine aromatic triad and reinforced electrostatic contacts that stabilized the bnAb/Env interface. These findings demonstrate that systematic in vitro engineering can generate bnAbs with enhanced breadth and potency, providing a generalizable strategy for developing therapeutic antibodies against highly diverse pathogens.

Accurate modeling of the structures of protein-protein complexes and other biomolecular interactions represents a longstanding and important challenge for computational biology. The Critical Assessment of PRedicted Interactions (CAPRI) experiment has served for over two decades as a key means to assess and compare current approaches and methods through blind predictive scenarios, highlighting useful strategies, and new developments. Here we describe the performance of our laboratory's team in recent CAPRI rounds, which included submissions for 10 modeling targets. Our team utilized a range of docking and modeling approaches, including ZDOCK, Rosetta, and ZRANK, to model, refine, and score protein-protein and protein-DNA complexes. For recent targets we utilized adaptations of AlphaFold to generate models, leading to near-native models for an antibody-peptide target, and a highly accurate (but low ranked) model for an antibody-MHC complex. These results underscore the utility of AlphaFold-based protocols for predictive protein complex modeling, including for immune recognition, and highlight considerations regarding the use of AlphaFold confidence metrics in model selection.

Anti-HIV envelope broadly neutralizing antibodies (bnAbs) are alternatives to conventional antiretrovirals with the potential to prevent and treat infection, reduce latent reservoirs, and/or mediate a functional cure. Clinical trials with "first-generation" bnAbs used alone or in combination show promising antiviral effects but also highlight that additional engineering of "enhanced" antibodies will be required for optimal clinical utility, while preserving or enhancing Current Good Manufacturing Practices (cGMP) manufacturing capability. Here, we report the engineering of an anti-CD4-binding site (CD4bs) bnAb, N49P9.3. Through a series of rational modifications, we produced a variant, N49P9.6-FR-LS, that demonstrates enhanced potency, superior antiviral activity in combination with other bnAbs, low polyreactivity, and longer circulating half-life. Additional engineering for manufacturing produced a final variant, eN49P9, with properties conducive to cGMP production. Overall, these efforts demonstrate the feasibility of developing enhanced anti-CD4bs bnAbs with greatly improved antiviral properties as well as potential translational value.

Global elimination of hepatitis C virus (HCV) will require an effective cross-genotype vaccine. The HCV E2 envelope glycoprotein is the main target of neutralizing antibodies but also contains epitopes that elicit non-neutralizing antibodies which may provide protection through Fc effector functions rather than direct neutralization. We determined cryo-EM structures of a broadly neutralizing antibody, a moderately neutralizing antibody, and a non-neutralizing antibody bound to E2 to resolutions of 3.8, 3.3, and 3.7 Å, respectively. Whereas the broadly neutralizing antibody targeted the front layer of E2 and the non-neutralizing antibody targeted the back layer, the moderately neutralizing antibody straddled both front and back layers, and thereby defined a new neutralizing epitope on E2. The small size of complexes between conventional (monovalent) Fabs and E2 (~110 kDa) presented a challenge for cryo-EM. Accordingly, we engineered bivalent versions of E2-specific Fabs that doubled the size of Fab-E2 complexes and conferred highly identifiable shapes to the complexes that facilitated particle selection and orientation for image processing. This study validates bivalent Fabs as new fiducial markers for cryo-EM analysis of small proteins such as HCV E2 and identifies a new target epitope for vaccine development.

The host machinery in the secretory organelles, the endoplasmic reticulum and the Golgi network, is pivotal in directing the biogenesis of enveloped virus proteins during natural infection and vaccination. Secretory trafficking of the viral proteins in these organelles is accompanied by post-translational modifications (PTMs), which modify interactions with host receptors and antibodies. Hence, elucidating the fundamental basis of PTM modulation by secretory trafficking is essential for designing genetic vaccines that encode stable and immunogenic viral proteins. For instance, the spike protein in COVID-19 mRNA vaccines undergoes bidirectional secretory trafficking after export from the endoplasmic reticulum to the cis-Golgi. This supplies the spike from the cis-Golgi to the coronavirus assembly site in the endoplasmic reticulum-Golgi intermediate compartment (ERGIC) by retrograde trafficking and to the plasma membrane by anterograde trafficking for coronavirus transmission and for immune display. However, surprisingly, little is known about the modulation of spike PTMs by this secretory recycling and retention in the endoplasmic reticulum-ERGIC-Golgi pathway before export to the plasma membrane. Since PTMs such as N-glycans modulate the conformations of immunogenic epitopes in the spike protein, addressing this knowledge gap in secretory routing is critical for understanding spike-immune system interactions. Furthermore, the bidirectional trafficking of the spike protein engages and diverts the secretory machinery, which can cause secretory stress, inflammation, and immune dysfunction. Hence, elucidating the fundamental interactions that govern spike biogenesis and trafficking will expedite the design of next- generation genetic vaccines that interfere minimally with the secretory trafficking machinery while undergoing proper PTMs for stability and efficient delivery to the plasma membrane. Here, we report atomic-level insights into the PTM and structural biology of novel spike protein constructs arrested in various stages of secretory trafficking. Using a combination of mass spectrometry, single particle cryoEM, and MD simulations on these differently arrested spike constructs, we identified clusters of N-glycans whose remodeling is intimately linked to spike recycling and secretory routing. These spike constructs demonstrate robust binding to a panel of neutralizing antibodies despite differences in their N-glycans and secretory routing to the plasma membrane. Finally, building on these structure-function insights, we engineered a novel spike vaccine candidate with enhanced secretion to the cell surface. We determined its structures in various conformational states by single particle cryoEM, demonstrating the presentation of immunogenic epitopes. Thus, our investigation provides novel strategies for designing a new generation of genetic vaccines with enhanced secretion and immune display at the host cell plasma membrane. Bottom of Form

DDX3 is an ATP-dependent RNA helicase involved in multiple cellular activities, including RNA metabolism and innate immunity. DDX3 is known to assist the replication of some viruses while restricting some others through direct interaction with the viral proteins. However, the role of DDX3 in the replication of the hepatitis E virus (HEV) is unknown. In this study, DDX3 is shown to interact with the HEV capsid protein and provide an indispensable role in HEV replication. The DDX3 C-terminal domain was demonstrated to interact with the capsid protein, which was previously demonstrated to inhibit the production of type I interferons. Knockdown of DDX3 compromised the capsid protein-mediated blockage of interferon induction. Notably, DDX3 silencing led to a significant reduction in HEV replication. Also, the ATPase activity of DDX3 is required for the HEV replication as an ATPase-null mutant DDX3 failed to rescue the viral replication in the DDX3-silenced cells. These results demonstrate a pro-viral role of DDX3 in HEV replication, providing further insights into the virus-cell interactions.