{"paper_id":"23b9f822-60ee-4e91-bc6d-6a6dd4242998","body_text":"Spike mutations that a ffect the function and \nantigenicity of recent KP.3.1.1-like SARS-CoV-2 \nvariants \nBernadeta Dadonaite1, Sheri Harari1, Brendan B. Larsen1, Lucas Kampman1,2, Alex Harteloo3, Anna \nElias-Warren3,  Helen Y. Chu3, Jesse D. Bloom1,4,#  \n1 Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, Washington, 98109, USA \n2 Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, WA \n3 University of Washington, Department of Medicine, Division of Allergy and Infectious Diseases, Seattle, WA \n4 Howard Hughes Medical Institute, Seattle, WA, 98195, USA \n# Lead Contact jbloom@fredhutch.org  \nAbstract \nSARS-CoV-2 is under strong evolutionary selection to acquire mutations in its spike protein that \nreduce neutralization by human polyclonal antibodies. Here we use pseudovirus-based deep \nmutational scanning to measure how mutations to the spike from the recent KP.3.1.1 SARS-CoV-2 \nstrain a ffect cell entry, binding to ACE2 receptor, RBD up/down motion, and neutralization by \nhuman sera and clinically relevant antibodies. The spike mutations that most a ffect serum antibody \nneutralization sometimes di ffer between sera collected before versus after recent vaccination or \ninfection, indicating these exposures shift the neutralization immunodominance hierarchy. The sites \nwhere mutations cause the greatest reduction in neutralization by post-vaccination or infection sera \ninclude receptor-binding domain (RBD) sites 475, 478 and 487, all of which have mutated in recent \nSARS-CoV-2 variants. Multiple mutations outside the RBD a ffect sera neutralization as strongly as \nany RBD mutations by modulating RBD up/down movement. Some sites that a ffect RBD up/down \nmovement have mutated in recent SARS-CoV-2 variants. Finally, we measure how spike mutations \naffect neutralization by three clinically relevant SARS-CoV-2 antibodies: VYD222, BD55-1205, and \nSA55. Overall, these results illuminate the current constraints and pressures shaping SARS-CoV-2 \nevolution, and can help with e fforts to forecast possible future antigenic changes that may impact \nvaccines or clinical antibodies. \nImportance \nThis study measures how mutations to the spike of a SARS-CoV-2 variant that circulated in early \n2025 a\nffect its function and recognition by both the polyclonal antibodies produced by the human \nimmune system and monoclonal antibodies used as prophylactics. These measurements are made \nwith a pseudovirus system that enables safe study of viral protein mutations using virions that can \nonly infect cells once. The study identi fies mutations that decrease recognition by current human \n1 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nantibody immunity; many of these mutations are increasingly being observed in new viral variants. It \nalso shows the importance of mutations that move the spike’s receptor binding domain up or \ndown. Overall, these results are useful for forecasting viral evolution and assessing which newly \nemerging variants have reduced recognition by immunity and antibody prophylactics. \nIntroduction \nOver the course of SARS-CoV-2 evolution in humans over the last half decade, the spike protein \nhas accumulated >60 amino-acid mutations (1–3). This evolution is driven by strong selective \npressure for spike to escape from the antibody immunity accumulating in the human population \n(4–7) while retaining its ability to bind ACE2 receptor (8,9) and mediate cell entry (10,11). New \nSARS-CoV-2 lineages carrying additional mutations in spike are constantly emerging, but it remains \nchallenging to predict which of these lineages have mutations that will enable them to be \nevolutionary successful. \nDeep mutational scanning is a powerful approach to measure how spike mutations a ffect \nkey functional and antigenic properties of spike (2,9,12–14), but the fact that both spike (8,15,16) \nand human population immunity (17–20) are constantly evolving limit the utility of measurements \nmade using older strains and human antibodies for understanding newer variants. Here, we use \npseudovirus-based deep mutational scanning (2,21) to measure how thousands of mutations to \nthe spike of the recent KP.3.1.1 variant a ffect cell entry, receptor binding, RBD up/down motion, \nand neutralization by human sera and therapeutic antibodies. Overall, our work provides detailed \nmaps of the functional and antigenic e\nffects of spike mutations that can help rationalize recent \ntrends in SARS-CoV-2 evolution and identify mutations that affect key protein properties.  \nResults \nPseudovirus-based deep mutational scanning of KP.3.1.1 spike  \nTo measure how mutations in the SARS-CoV-2 spike affect cell entry, receptor binding and escape \npolyclonal sera or therapeutic antibodies, we used pseudovirus-based deep mutational scanning  \n(Fig. 1A) (2,21). This method produces genotype-phenotype linked lentiviral particles that encode \nuniquely barcoded spike variants and can be used to measure the e ffects of mutations on different \nspike phenotypes (21) (Fig. S1A). Because these pseudoviruses are restricted to a single round of \ninfection and require helper plasmids to produce viral particles, they cannot cause disease or \ntransmit in humans, making them a safe tool for characterising mutations in viral proteins at \nbiosafety-level-2.  \n We designed pseudovirus-based deep mutational scanning libraries for the spike protein \nfrom the recently circulating KP.3.1.1 strain. KP.3.1.1 is a descendant of the JN.1 lineage and was \none of the major variants circulating from the second half of 2024 to early 2025 (22). Its spike \nshares many important antigenic mutations with the other current JN.1 descendant strains, and is \n2 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nclosely related to the spikes currently recommended as options for inclusion in SARS-CoV-2 \nvaccines (JN.1, KP.2, and LP.8.1)  (Fig. 1B) (23).  \nWe designed the deep mutational scanning libraries to contain all evolutionarily accessible \nand antigenically important mutations in the spike protein. Speci fically, we included all mutations \nthat have occurred at appreciable frequency during the SARS-CoV-2 evolution in humans, as well \nas every possible amino-acid change at sites that have mutated frequently in recent variants and all \nsites within the RBD. We produced two independent pseudovirus libraries  (Lib-1 and Lib-2), which \ncontained 42,783 and 45,513 barcoded variants, respectively, and covered 95% of the 9,809 \ntargeted amino-acid mutations with an average of 1.3 mutations per spike (Fig. S1B-C).   \nMutation e\nff\nects on spike-mediated cell entry \nWe measured how mutations to KP.3.1.1 spike a ffect entry into 293T cells that were engineered to \nexpress medium levels of the ACE2 receptor (24) (Fig. 2A and interactive heat map at \nhttps://dms-vep.org/SARS-CoV-2_KP .3.1.1_spike_DMS/cell_entry.html). The measured e ffects of \nmutations on cell entry were highly correlated between the two independent libraries (Fig. S1D). As \nexpected stop codons were highly deleterious for cell entry whereas amino acid mutations had \nvaried effects (Fig. 2A). Single-residue deletions were well tolerated at many sites in the N-terminus \ndomain (NTD), consistent with frequent NTD deletions in many circulating SARS-CoV-2 variants \n(25) (Fig. 2A). Amino-acid mutations in the RBD had a range of e\nffects, with some sites intolerant \nof mutations but others tolerant of many changes. \nOur measurements suggest a possible reason why certain mutations have begun to \nrecurrently evolve in recent JN.1-descended strains related to KP.3.1.1 after being rare in earlier \nvariants. A number of these mutations—speci fically T22N, K182R, G184S, F186L, R190S, A435S, \nand N487D—are better tolerated for cell entry in the KP.3.1.1 spike compared to the earlier \npre-JN.1 XBB.1.5 lineage (Fig. 2B), as assessed by comparing our current deep mutational \nscanning to prior measurements for the XBB.1.5 spike (2). Therefore, shifts in mutational tolerance \nfor specific mutations may be a contributor to the recent recurrent selection for these mutations. \nMutation e\nff\nects on ACE2 binding \nTo determine how spike mutations a ffect receptor binding, we measured how well each spike \nmutant pseudovirus was neutralized by soluble monomeric ACE2 protein (Fig. S2A). We and \nothers have previously shown that ACE2 binding a ffinity to spike is proportional to neutralization of \nSARS-CoV-2 pseudovirus by soluble ACE2 protein (2,26,27). Namely, mutations that increase \nspike’s binding to ACE2 also increase pseudovirus neutralization by soluble ACE2 protein, and \nmutations that decrease spike’s ACE2 binding decrease pseudovirus neutralization by soluble \nACE2. Therefore, incubating deep mutational scanning libraries with increasing amounts of \nmonomeric ACE2 protein allows us to measure how mutations a ffect ACE2 binding. Note that this \napproach only works for spike mutants that retain at least some moderate ability to mediate \npseudovirus entry in ACE2-expressing cells. Among the spike mutations that retain su fficient cell \nentry function, e ffects on cell entry and ACE2 binding show no correlation (Fig. S2B), \n3 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\ndemonstrating that cell entry and ACE2 binding are distinct phenotypes, and ACE2 binding is often \nnot the limiting factor for cell entry in our assays. \nA variety of mutations both in the RBD and other regions of spike a ffect ACE2 binding, as \nmeasured by soluble ACE2 neutralization (Fig. 3A-B and interactive heatmap at \nhttps://dms-vep.org/SARS-CoV-2_KP .3.1.1_spike_DMS/receptor_binding.html). The substantial \neffect of some mutations outside the RBD on ACE2 binding is because the interaction of the full \nspike with ACE2 is impacted by several distinct mechanisms: direct interaction of the RBD with \nACE2, changes in RBD up (open) or down (closed) conformation, and changes to S1 shedding \n(28–31). Interestingly, we measure mutations at sites distant to the RBD’s ACE2 binding motif to \nhave as large e ffects on ACE2 binding as mutations at sites in close proximity to ACE2, \nemphasizing the importance of conformational changes to spike in a ffecting ACE2 binding (Fig. \n3B). Many ACE2 distal RBD mutations with the strongest binding e ffects are at sites near the base \nof the RBD in spike, suggesting their likely involvement in positioning the RBD in the up or down \nconformation (eg, sites 332, 358, 390, 393, 395, 517 and 527; Fig. 3A-B and Fig S2C). Among \nthe sites in proximity to ACE2, certain mutations at site E493 cause the largest increase in receptor \nbinding (Fig. 3A-B). Notably site 493 interacts with ACE2 directly, recently substituted from Q to E \nin parents of KP.3.1.1 and several other current lineages, and has been previously shown to \nepistatically interact with two other recent mutations also present in KP.3.1.1 (L455S and F456L) \n(7,16).  \n There is a good correlation between the e ffects of RBD mutations on ACE2 binding in our \nKP.3.1.1 deep mutational scanning and similar data previously published for the XBB.1.5 spike (2) \n(Fig. 3C). However, there are some mutations with di fferent effects on ACE2 binding in KP.3.1.1 \nand XBB.1.5, including A435S which increases binding to ACE2 in KP.3.1.1 but decreases binding \nfor XBB.1.5 (note this mutation also had contrasting e ffects on cell entry in the two spikes as \ndescribed above) (Fig. 3D). The A435S mutation has been rare for most of SARS-CoV-2’s \nevolution, but has recently occurred independently in multiple lineages including the \nJN.1-descendants NB.1.8.1, XEC.25, MC.10.1, MC.31, and NP.1 variants and a recent \nBA.3-descendant saltation variant BA.3.2. In addition, E493D and E493N increase ACE2 binding \nby the KP.3.1.1 spike, but in XBB.1.5 mutating site 493 from its initial identity of Q to any of E, D, or \nN impairs ACE2 binding (Fig. 3D) (2,32).  \nMutation e\nff\nects on serum neutralization  \nWe measured how spike mutations a ffect neutralization by sera collected from seven human \nindividuals pre- and post-exposure by vaccination or infection with JN.1-descendant variants \n(Table S1). All seven individuals were adults who had originally been imprinted by vaccination with \nthe early COVID-19 vaccine in 2021 followed by various further booster vaccinations and \ninfections. For most (although not all) of these individuals, exposure to a JN.1-descendant spike via \nvaccination increased neutralizing serum titers against KP.3.1.1 (Fig. S3A-B).  \n We used the pseudovirus libraries to measure how the KP.3.1.1 spike mutations a ffected \nneutralization by the sera from each individual both pre- and post-vaccination or infection with a \n4 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nJN-1 descendant spike. For the most part, mutations had similar e ffects on neutralization by sera \nfrom each individual collected pre- versus post-vaccination or infection (Fig. 4 and S3C). Across all \nsera, the sites where mutations caused the most escape from serum neutralization were primarily in \nthe RBD (Fig. 4 and interactive plot at \nhttps://dms-vep.org/SARS-CoV-2_KP .3.1.1_spike_DMS/polyclonal_sera_escape.html). RBD \nmutations at sites 332, 344, 357, 393, 428, 458, 470 and 518 caused the greatest serum escape \nboth pre- and post-vaccination or infection (Fig. 4A). Some sites outside the RBD also reduced \nserum neutralization, including sites 50, 132, 200, 222 in NTD, 572 in SD1, and 852 in S2. Notably, \nmost of the sites where mutations caused the greatest escape in the RBD and all the strongest \nsites of escape outside the RBD are ones where mutations a ffect ACE2 binding (Fig. 3B, and next \nsection), suggesting mutations at these sites impact serum neutralization largely changing the \nRBD’s up/down conformation, thereby indirectly a ffecting binding by antibodies targeting potent \nneutralizing epitopes on the RBD (2,33–35). However, there are also some sites of appreciable \nescape where mutations do not a\nffect RBD up/down binding (e.g., 456, 458, 475, 478, 487); these \nmutations likely directly escape binding by neutralizing antibodies rather than a ffecting RBD \nup/down conformation. \nWhile many mutations that reduce serum neutralization pre- and post-vaccination or \ninfection were shared among the di fferent sera, in a subset of individuals exposure to a \nJN.1-descendant spike clearly shifts neutralization immunodominance. In adult-1, adult-3, and \nadult-4 and adult-5, several RBD sites where mutations had little or no e ffect on serum \nneutralization before JN.1-descendant spike exposure become the dominant escape sites after \nvaccination or infection (Fig. 4B). These new escape sites include 403, 405, 475, 478, 487, 490 \nand 505. Notably, in circulating SARS-CoV-2 variants, many of these sites have recently acquired \nmutations that reduce serum neutralization. For example, the XFJ, JN.1.18.5, LF.7.1.2, LF.7.2.1, \nPC.2 and LP.8.1.9 variants all carry A475V, BA.3.2 carries K478N  while NB.1.8.1 carries K478I, \nand XFG carries N487D.  \nWe validated the deep mutational scanning measurements of how mutations a ffect serum \nneutralization using standard SARS-CoV-2 pseudovirus neutralization assays (Fig. S4) (36). The \ndeep mutational scanning measurements correlated well with changes in IC50 values measured in \nthe standard neutralization assays (Fig. S4A). We also con\nfirmed via standard neutralization assays \nthat mutations A475V, H505E, K478I and N487D cause a larger reduction in the neutralization by \nthe serum from some individuals after versus before exposure to a JN.1-descendant spike (Fig. \nS4B), consistent with the deep mutational scanning.  \nSites where mutations a\nff\nect RBD up/down conformation \nTo identify sites in spike that a ffect RBD up/down conformation, we leveraged the previously noted \nfact that mutations at these sites have opposing e ffects on ACE2 binding and serum antibody \nneutralization escape: namely, mutations that put the RBD more in the up conformation increase \nACE2 binding but also enhance neutralization (2,33–35). Our measurements for the KP.3.1.1 spike \nshow this relationship clearly: there is a strong inverse correlation between serum neutralization \n5 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nescape and ACE2 binding for mutations that a ffect both these phenotypes but are distal from the \nRBD’s ACE2-binding motif (Fig. 5A). This inverse correlation is due to the fact that positioning RBD \nin the up conformation reveals the receptor-binding motif, which mediates binding to ACE2 but is \nalso targeted by many potent neutralizing antibodies. Therefore, mutations that put the RBD more \nin the up conformation sensitize the spike to serum neutralization (negative escape values in our \nmeasurements), while mutations that put the RBD more in the down conformation tend to cause \nserum neutralization escape. By contrast, ACE2-proximal sites show no correlation between ACE2 \nbinding and serum neutralization (Fig. 5A) because they often both interact with the receptor \ndirectly and are directly targeted by neutralizing serum antibodies. Note, that some ACE2 proximal \nsites may still modulate the RBD up/down conformation, but this modulation does not lead to the \naforementioned consistent pattern on ACE2 binding and neutralization because the direct e\nffects of \nmutations at these sites both ACE2 binding and neutralizing antibody binding can overwhelm the \neffect of the RBD up/down conformation modulation. \nTo estimate how much each site a ffects RBD up/down conformation, we calculated the \ncorrelation between serum neutralization escape and ACE2 binding at each site, weighting it by the \nroot mean square e ffect of mutations at each site on both phenotypes (Fig. 5B and interactive plot \nat https://dms-vep.org/SARS-CoV-2_KP .3.1.1_spike_DMS/RBD_movement.html). Among the \nsites that stand out as strongly a ffecting RBD up/down conformation are many clade-de fining \nmutations as well as some of the most frequently mutated sites through various periods of \nSARS-CoV-2 evolution in humans. Site 222 was one of the most frequently mutated sites just \nbefore Omicron emerged (37), sites 371 and 373 fixed mutations in all Omicron lineages (38), and \nsites 332, 356 and 570 fixed mutations in the BA.2.86-lineage which is the ancestor of most \ncurrently circulating strains (39). The prevalence of mutations at sites that modulate RBD up/down \nconformation in major SARS-CoV-2 lineages suggests a strong selective pressure to balance \nreceptor binding with resistance to neutralization by RBD-directed antibodies; indeed evidence \nsuggest that multiple recent SARS-CoV-2 variants have acquired mutations that position the RBD \nin a more closed conformation (34,40).  \nE\nff\nects of mutations on neutralization by clinically relevant monoclonal antibodies \nWe next determined how mutations to spike a ffect neutralization by three clinically relevant \nmonoclonal antibodies: BD55-1205 (12), SA55 (41), and VYD222 (42) (Fig. 6). BD55-1205 and \nSA55 have maintained high neutralizing potency against currently circulating variants (12). SA55 is \nin clinical trials in China (41), BD55-1205 is licensed to Moderna Inc. (12), and VYD222 is currently \nthe only SARS-CoV-2 antibody authorized for use in the USA for pre-exposure prophylaxis in \nimmunocompromised individuals (it is the antibody in Pemivibart) (44). Knowledge of which \nmutations reduce neutralization by these antibodies is important for ongoing surveillance, as all \nother clinically approved SARS-CoV-2 antibodies have now been escaped by viral mutations \n(41,45). \nAll three antibodies bind to sites around the RBD’s receptor binding motif, with SA55 and \nVYD222 sharing especially similar structural epitopes (41,46). Our deep mutational scanning shows \n6 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nthat all three antibodies are strongly a ffected by mutations at several sites in the range from 500 to \n505, although the exact sites in this range where mutations have the most impact varies among the \nantibodies (Fig. 6). BD55-1205 neutralization is also a ffected by mutations at sites 456, 475 and \n493, all of which interact with ACE2 (Fig. 6A). Most changes to 456 and 475 sites are deleterious \nfor ACE2 binding (see letter colors in logoplots in Fig 6), although A475V, which measurably \nescapes BD55-1205, is only mildly deleterious for ACE2 binding and has recently occurred in \nseveral JN.1-descendant lineages. SA55 is a ffected by mutations to sites 440 and 445, and to a \nlesser degree by mutations at 493 (Fig. 6B). VYD222 is also a ffected by mutations at site 440 in \naddition to mutations at sites 405 and 403 (Fig. 6C). Because sites 500-505 are primarily \naccessible in the RBD’s up position, all three antibodies are a\nffected by mutations that modulate \nRBD up/down movement, as has been noted previously (12,47,48). In particular, some mutations \nat sites 332, 357, and 427 a ffect neutralization by all three antibodies to various degrees, despite \nthe fact none of these sites are in the direct structural epitopes, presumably by putting the RBD \nmore in the down conformation and so partially shielding the antibody epitopes.  \nInterestingly, in our pseudovirus deep mutational scanning, mutations at site 505 cause \nsignificantly more escape from all three of the antibodies than reported in previously published \nyeast-based RBD-only deep mutational scanning data suggest (Fig. S5) (6,41). We hypothesize \nthat this di fference is because RBD-only assays measure just the direct e ffects of mutations on \nantibody-RBD binding, whereas the pseudovirus deep mutational scanning also measures the \nimpacts of mutations on RBD up/down movement that a ffect RBD epitope accessibility in the \ncontext of full spike. Indeed, mutations at RBD motion-regulating sites 332, 357 and 427 a ffect \nneutralization by all three antibodies in full-spike but not in yeast-based RBD-only deep mutational \nscanning (Fig. S5). Similarly, mutations at site 505 both directly a ffect antibody-RBD binding and \nthe up/down motion of the RBD due to this site’s location in the inter-protomer interface in the \ndown RBD spike conformation. While site 505 is likely under signi ficant evolutionary constraint \nbecause most mutations at that site reduce ACE2 binding, our serum-escape measurements \ndescribed above suggest this site may be starting to come under appreciable pressure for \nmutations from population immunity. \nDiscussion \nHere we have measured how mutations to the KP.3.1.1 spike a ffect several distinct phenotypes: \ncell entry, ACE2 binding, serum neutralization, RBD up/down motion, and neutralization by key \nmonoclonal antibodies. These measurements provide several important insights into the selection \npressures and molecular constraints currently shaping SARS-CoV-2  evolution. \n First, our measurements underscore the substantial impact of mutations that a ffect RBD \nup/down motion on receptor binding and antibody neutralization. In the context of the full spike, \nmutations that a ffect RBD up/down motion impact ACE2 binding as much as mutations at RBD \nsites that interact with ACE2 directly. Mutations that a ffect RBD up/down motion have a consistent \nsignature: they have opposite e ffects on ACE2 binding and serum neutralization, since putting the \n7 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nRBD more up increases the accessibility of the receptor-binding motif to bind ACE2 but also \nmakes it more susceptible to RBD-targeting neutralizing antibodies (31,49). Many sites that a ffect \nRBD up/down motion have mutated in major lineages during the course of SARS-CoV-2 evolution \nin humans, emphasizing the importance of the balancing e ffects of RBD up/down movement on \nviral fitness via impacts on ACE2 binding and serum neutralization. Note that mutations that put the \nRBD in a more up conformation may promote the cross-species transfer of coronaviruses by \nincreasing binding to receptors from new species (30,50,51); it appears that the spike of the first \nSARS-CoV-2 strains identi fied in humans had the RBD in a relatively more up conformation, and \nsubsequent evolution has selected for mutations that position the RBD more down (34,40).  \nSecond, our measurements identify sites where mutations cause the largest reductions in \nneutralization by human serum antibodies; there are already newly emerging viral lineages that \ncarry some of these mutations. Most of the sites where mutations most impact serum \nneutralization are in the RBD as expected from prior work showing that RBD-directed antibodies \nare usually responsible for most serum neutralizing activity (52–54), although mutations at some \nNTD sites also have a substantial e ffect. Some of the top RBD sites of serum antibody escape are \nlikely directly in the epitopes of neutralizing antibodies that sterically block receptor binding (e.g., \nsites 456, 458, 475, 478, 487); mutations at some of these sites have recently been observed in \nnew SARS-CoV-2 lineages. However, mutations at NTD and RBD sites that a ffect RBD up/down \nmotion and so a ffect serum neutralization indirectly by conformational masking, often have as \nmuch impact on serum neutralization as direct escape mutations in key RBD epitopes. As \nmentioned above, some of these up/down affecting sites have mutated in major lineages; however, \nsuch conformational escape is constrained by the fact that mutations that reduce serum \nneutralization by putting the RBD in a more down conformation also reduce ACE2 binding, and so \nmay need to be buffered by other ACE2 affinity increasing mutations.  \nThird, we find that exposure to a JN.1-descendant spike (via vaccination or infection) often \nshifts the neutralization immunodominance hierarchy to new epitopes. Speci fically, for some \nindividuals, vaccination or infection with a JN.1-descendant variant leads to mutations at new sites \ncausing large reductions in neutralization; these new sites include several (e.g., 475, 478, and 487) \nthat have acquired mutations in very recent SARS-CoV-2 lineages. Our data cannot determine the \nunderlying mechanism responsible for this shift in serum neutralizing speci\nficity. Once individuals \nhave been imprinted by SARS-CoV-2 infection or vaccination, most of the neutralizing response to \nsubsequent vaccinations and infections is driven by activation of pre-existing cross-reactive B cells \n(26,55–58). However, the a ffinity maturation of these pre-existing B cells can shift the balance of \nepitope targeting in polyclonal sera (56). In addition, su fficient exposures to new variants can \nactivate naive B cells (58,59). The shifts in serum neutralizing speci ficity we observe after exposure \nto a JN.1-descendant variation could be due to some combination of boosting of pre-existing \ncross-reactive B-cells that were previously subdominant, a ffinity maturation of pre-existing B-cells \nto better target recently mutated epitopes, or activation of naive B-cells targeting new epitopes. \nRegardless of the underlying mechanism, the fact that exposure to recent variants changes the \nneutralization immunodominance hierarchy supports the idea that updating vaccines to more \n8 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nrecently circulating variants (23) can shift the speci ficity of neutralizing antibodies to target newer \nSARS-CoV-2 variants. \nThe fact that exposure to recent JN.1-descendant variants can shift which spike mutations \naffect neutralization by the serum antibodies of imprinted adults highlights the increasing \nheterogeneity in antibody immunity across the human population. We recently showed that the \nepitopes targeted by the neutralizing antibodies of young children who had experienced just a \nsingle infection with a recent variant di ffer dramatically from those targeted by adults imprinted by \ninfection or vaccination early in the SARS-CoV-2 pandemic (20). The current study only examined \nserum from imprinted adults, but finds heterogeneity even among such adults depending on \nwhether they have been exposed to a JN.1-descendant variant. This increasing immune \nheterogeneity across the population may favor more co-circulation of multiple SARS-CoV-2 \nlineages rather than repeated rapid evolutionary sweeps by a single variant (60,61). \nWe also mapped how mutations a ffect neutralization by three clinically relevant monoclonal \nantibodies (BD55-1205, SA55 and VYD222) that have so far retained neutralizing activity against \nnearly all SARS-CoV-2 lineages. These antibodies target functionally constrained RBD epitopes \nthat overlap with the ACE2 binding motif and are only fully accessible in the up RBD conformation, \nand our data show that neutralization by all three antibodies is reduced by mutations that put the \nRBD in a more down conformation. In particular, mutations to site 505, which both a\nffects RBD \nmotion and forms part of the epitope for all three antibodies, have a greater impact on pseudovirus \nneutralization than was apparent in prior RBD-only yeast-display deep mutational scanning (6). Site \n505 remains under substantial constraint, since most mutations at that site both reduce direct \nRBD-ACE2 binding a ffinity (16) and put the RBD in a more up conformation that increases its \nsusceptibility to RBD-directed serum neutralizing antibodies. However, our results show that site \n505 is now a serum neutralization escape mutation for some individuals who have been exposed to \na JN.1-descendant variant, suggesting such individuals now produce appreciable neutralizing \nantibodies directly targeting site 505. Therefore, site 505 might be under increasing pressure to \nmutate in circulating SARS-CoV-2 lineages, although additional changes to spike would likely be \nneeded to overcome the pleiotropic e\nffects such a mutation would have on ACE2 binding and RBD \nup/down conformation.    \nAcknowledgements \nWe thank David Veesler from University of Washington for providing soluble ACE2 protein. We thank Ryan Hisner and \nFederico Gueli for useful comments on the manuscript. This research was funded by grants from the NIAID/NIH awarded \nto JDB: P01AI167966 and the SAVES program (contract 75N93021C00015, option 18.C). JDB is an investigator at the \nHoward Hughes Medical Institute. This research was also supported by the Genomics & Bioinformatics Shared \nResource, RRID:SCR_022606, of the Fred Hutch/University of Washington Cancer Consortium (P30 CA015704), by the \nFlow Cytometry Shared Resource, RRID:SCR_022613, of the Fred Hutch/University of Washington/Seattle Children’s \nCancer Consortium (P30 CA015704), and by Fred Hutch Scienti\nfi\nc Computing, NIH grants S10-OD-020069 and \nS10-OD-028685. SH is a postdoctoral fellow of the Translational Data Science Integrated Research Center at the Fred \nHutchinson Cancer Center. BBL is a Washington Research Foundation postdoctoral fellow. This material is based upon \nwork supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-2140004. Any \nopinion, \nfi\nndings, and conclusions or recommendations expressed in this material are those of the authors and do not \n9 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nnecessarily re\nfl\nect the views of the National Science Foundation. This manuscript is the result of funding in whole or in \npart by the National Institutes of Health (NIH). It is subject to the NIH Public Access Policy. Through acceptance of this \nfederal funding, NIH has been given a right to make this manuscript publicly available in PubMed Central upon the O\nffi\ncial \nDate of Publication, as de\nfi\nned by NIH. \n \nCompeting interests \nJDB consults for Apriori Bio, Invivyd, GSK, P\nfi\nzer, and the Vaccine Company. JDB and BD are inventors on Fred Hutch \nlicensed patents related to viral deep mutational scanning. HYC has served on advisory boards for Merck, Roche, Vir, \nand Abbvie. \nMethods \nData availability and interactive fi\ngures \nAll data described in this manuscript are available as raw numerical values and in various interactive fi\ngure formats: \n- Interactive fi\ngures can be found at a website associated with this manuscript \nhttps://dms-vep.org/SARS-CoV-2_KP .3.1.1_spike_DMS/. The website homepage has interactive fi\ngures \norganised by phenotype and by clicking on each phenotype the reader can fi\nnd key plots, descriptions and \nlinks to raw numerical values used to make the interactive plots.  \n- The computational analysis pipeline used to analyse deep mutational scanning data and make all associated \nmanuscript fi\ngures is on GitHub at https://github.com/dms-vep/SARS-CoV-2_KP .3.1.1_spike_DMS . \nSequencing data associated with this manuscript has been deposited to the SRA under BioProject PRJNA1305008..  \nDeep mutational scanning library design \nDeep mutational scanning libraries were designed to cover all possible mutations in the RBD and all tolerated and \nfrequently mutated changes outside the RBD. To identify tolerated and frequently mutated sites we included mutations \nthat occur more than 50 times among SARS-CoV-2 genomes deposited on GISAID (62), mutations that occur at least 10 \ntimes on UShER (63) spike phylogenetic tree, any mutation present in a recent SARS-CoV-2 lineage (at the time of library \ndesign these lineages where BA.2.86, JN.1, JN.1.11.1, and KP.3), and any mutations that occurred at least once in a \nPango designated lineage (64). In addition, we introduced all possible amino-acid mutations at sites that fi\nt any of the \nfollowing criteria: mutated at least 50 times in a recent SARS-CoV-2 lineage, mutated along UShER spike phylogenetic \ntree at least 2500 times, mutated repeatedly at least 3 times among any Pango designated lineages, or had mutated in \nKP.3.1.1 variant relative to Wuhan-Hu-1 sequence. The above criteria were also applied for deletions but deletions were \nonly included if they were present at any site in the NTD or positions 331-354 or 434-508 in the RBD. Several mutations \nand sites to saturate were also included manually in library design regardless of their frequency counts based on reports \nof these mutations occurring in circulating lineages at the time of library design. The list of manually included mutations \nas well as parameters for all other selection criteria is at \nhttps://github.com/dms-vep/SARS-CoV-2_KP .3.1.1_spike_DMS/blob/main/library_design/con\nfi\ng.yaml. The full list of all \nmutations included in the library design is at https://github.com/dms-vep/SARS-CoV-2_KP .3.1.1_spike_DMS/blob/main/ \nlibrary_design/results/mutations_to_make.csv .  \n \nOverview of library construction using Golden Gate assembly \nGolden Gate assembly was used to create KP.3.1.1 spike coding plasmid libraries containing all the designed mutations \n(65–71) (Fig. S6). Due to the length of the spike sequence and the number of mutations we wanted to include in the \nlibrary it was cost-prohibitive to synthesize the spike gene as a single fragment for all spike variants we wanted to include. \n10 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nWe therefore subdivided spike into 17 overlapping tiles between 250-290 nt in length (close to the maximum length that \ncan be synthesized by Twist Bioscience as a single-stranded DNA (ssDNA) oligo pool) (Fig. S6A), computationally \ndesigned a pool of oligos, where each oligo is one of the tiles with a mutation we wanted to include in the library (Fig. \nS6B), and ordered all the oligos pooled together as ssDNA fragments from Twist Bioscience. From that ssDNA pool we \nperformed 17 individual PCR reactions to amplify oligos belonging to each tile using primers with fl\nanking sequences \ncontaining BsmBI restriction sites (Fig. S6C). Golden Gate assembly was then used to assemble each tile pool and \nfl\nanking spike sequences unique to each tile into a shuttle vector (Fig. S6D). The assembled shuttle vector pool was \nelectroporated into bacteria and next day plasmids were recovered for all 17 pools. The full spike sequence was ampli\nfi\ned \nfrom each pool using primers with fl\nanking sequences that match lentiviral backbone as well as a barcode sequence in \nthe reverse primer (Fig. S6E). All 17 barcoded spike pools were then pooled equimolarly and HiFi assembly was used to \nclone the library (Fig. S6F), which after pooling had all designed mutations throughout the spike, into a lentivirus \nbackbone.   \nThe sequence of the codon optimized KP.3.1.1 spike in the fi\nnal lentiviral backbone used to make \npseudovirus-based libraries is at https://github.com/dms-vep/SARS-CoV-2_KP .3.1.1_spike_DMS/blob/main/library_ \ndesign/data/4838_pH2rU3_ForInd_KP .3.1.1_sinobiological_CMV_ZsGT2APurR.gb. Sequences for all 17 overlapping tiles \nare at https://github.com/dms-vep/SARS-CoV-2_KP .3.1.1_spike_DMS/blob/main/library_design/data/KP311_GAA \n_assembly_fragments.csv. Tiles were designed manually making sure that the overhangs for the fragments that will be \nassembled during the Golden Gate assembly step are unique for each fragment and have a sequence compatible with \nhigh fi\ndelity assembly (72). The 1st and the 17th tile overlapped with a pGGAselect DNA shuttle vector that is provided in \nNEBridge® Golden Gate Assembly Kit (BsmBI-v2) (E1602L). The oligo pool was designed using a script available at \nhttps://github.com/jbloomlab/gga_codon_muts_oligo_design. The script reads in tile sequences and desired mutation \nspreadsheet and generates a fasta fi\nle with oligo sequences that can be uploaded directly for ordering oligo pool from \nTwist Biosciences. We set the oligo design script to intentionally include 0.005 fraction of unmutated sequences for each \ntile in order to have some wild-type KP.3.1.1 spike in the \nfi\nnal pseudovirus library, as well as avoid any mutation design \nthat would introduce BsmBI cut sites. Sequences for designed oligos covering all 17 tiles is at \nhttps://github.com/dms-vep/SARS-CoV-2_KP .3.1.1_spike_DMS/blob/main/library_design/results/mutagenesis_oligos.fa. \nA GitHub repository that selects the mutations to be included in the library and designs mutated oligos for each tile is at \nhttps://github.com/dms-vep/SARS-CoV-2_KP .3.1.1_spike_DMS/tree/main/library_design.  \nDeep mutational scanning plasmid library cloning using Golden Gate Assembly \nTo amplify individual tile pools from one ssDNA oligo pool we performed 17 PCR reactions. For each reaction we used \nKOD Hot Start Master Mix (Sigma-Aldrich, Cat. No. 71842), 0.3 µM of forward and reverse primer and 2 ng of ssDNA \noligo pool. Each reaction was started at  95°C for 2 min and then went through 23 cycles of 95°C for 20 s, 62°C for 10 s, \n68°C for 25 s. To amplify \nfl\nanking spike sequences for each tile we used KOD Hot Start Master Mix, 0.3 µM of forward \nand reverse primer and 1 ng of KP.3.1.1 spike coding lentiviral backbone (see above section for plasmid map). The full list \nfor forward and reverse primers used in both reactions is at https://github.com/dms-vep/SARS-CoV-2_KP .3.1.1_spike \n_DMS/blob/main/library_design/data/primers.csv. Expected size products were gel and Ampure XP bead puri\nfi\ned (1:3 \nDNA to bead).  \nWe then performed Golden Gate assembly using NEBridge Golden Gate Assembly Kit (BsmBI-V2). For the \nassembly we used 100 fmol of ampli\nfi\ned tile pool and fl\nanking spike sequence fragments each and 50 fmol of \npGGAselect shuttle plasmid (provided in NEBridge Golden Gate Assembly Kit). The assembly reactions were incubated \nat 42°C for 1 min followed by 16°C for 1 min for 30 cycles, followed by 60°C for 5 min. The reactions were then puri\nfi\ned \nusing Ampure XP beads and eluted in 20 µl of water. 1 µl of puri\nfi\ned assembly was then used to electroporate NEB® \n10-beta Electrocompetent E. coli cells (C3020K). Electroporated cells were then suspended in 1 ml of recovery media \nand shaken at 37°C for 1 hour. After recovery, cells were spun down, recovery media was removed and cells were \nresuspended in chloramphenicol-containing LB media for incubation at 37°C with shaking overnight. High transformation \ne\nffi\nciency (~1 million colonies per tile library) was con\nfi\nrmed by diluting a small amount of recovered cells, plating on \nchloramphenicol-containing agar plates overnight and counting colony forming units the next day. High transformation \ne\nffi\nciency at this and later steps is important to avoid any barcode duplication at later virus production steps due to \n11 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nlentivirus recombination. Note also that here and in later electroporation steps we used liquid cultures to amplify our \nplasmid libraries as opposed to high-density spread on bacterial culture plates we used in the past as this has been \nshown to be su\nffi\ncient for a uniform plasmid ampli\nfi\ncation (73). After overnight growth, shuttle plasmid libraries for each \ntile were recovered using QIAprep Spin Miniprep Kit (Cat. No. 27106X4). \nNext, the spike libraries for each tile were ampli\nfi\ned and barcoded. We performed PCR on each tile plasmid \nlibrary using KOD Hot Start Master Mix, 10 ng of plasmid library and 0.3 µM of forward \n(5′-gcacgcgCAGCCGAGCCACATCGCTCA-3′) and reverse (5′- \ngcggaactccactaggaacatttctctctcgaaTCTAGANNNNNNNNNNNNNNNNAGATCGGAAGAGCGTCGTGTAGGGAAAGAG-3′) \nprimers, the latter primer contained a 16 nt barcode. After ampli\nfi\ncation each spike tile library was puri\nfi\ned by gel and \nAmpure XP beads. Note gel puri\nfi\ncation at this step is important because we found cloning of some tiles produces a \nminor amount of truncated spike and gel puri\nfi\ncation allowed us to recover only the full length products. All barcoded \nspike libraries were then pooled equimolarly. We made two equimolar pools of barcoded spike libraries to make library-1 \nand library-2 biological replicates. All subsequent steps in library production were done in parallel for library-1 and \nlibrary-2. NEBuilder® HiFi DNA Assembly Master Mix (E2621S) was then used to assemble barcoded spikes into a \nlentivirus backbone, as described previously (21). See lentivirus backbone structure in Fig. S6F; plasmid for the \nbackbone is available at Addgene #204579). Assembled backbones were electroporated in electrocompetent bacteria \nand plasmids were ampli\nfi\ned using liquid culture, as described above. As before we con\nfi\nrmed high electroporation \ne\nffi\nciency at this step and cultured at least 10 million colony forming units per library replicate.   \nProduction of cell-stored deep mutational scanning libraries \nTo produce the cell-stored deep mutational scanning libraries we used a method described previously (Fig. S1A) (21). In \nbrief, we fi\nrst used lentivirus backbones that carried barcoded spike libraries to produce VSV-G pseudotyped viruses. To \ndo so we transfected two 6-well plates of 293T cells with lentivirus helper plasmids (BEI: NR-52517, NR-52519, \nNR-52518) and VSV-G expression plasmid (Addgene #204156). 48 hours after transfection we collected VSV-G \npseudotyped viruses from cell supernatant and used them to infect 293T-rtTA cells at low multiplicity of infection (<0.01) \nso that most infected cells were infected with only one viral variant. We then used puromycin to select for successfully \ntransduced cells. The transduced cell library pool was then expanded and frozen at >15 M cells per aliquot in liquid \nnitrogen until further use. \nLong-read sequencing for variant-barcode linkage \nTo build a variant to barcode lookup table for the deep mutational scanning libraries, we rescued VSV-G pseudotyped \nviruses from the cell-stored libraries. We use VSV-G pseudotyping at this stage to rescue all virus variants from the cells \nregardless of how deleterious a mutation in spike may be. To do so we transfected library cells with lentivirus helper \nplasmids and VSV-G expression plasmid and 48 hours after transfection we recovered VSV-G pseudoviruses from cell \nsupernatant, puri\nfi\ned them from cell debris using 0.45 µm SFCA Nalgene 500mL Rapid-Flow fi\nlter unit (Cat. No. \n09-740-44B), and concentrated using Pierce Protein Concentrator (ThermoFisher, 88537). We then used ~10 million \ntranscription units of VSV-G pseudotyped viruses to infect 293T cells and 15 hours after infection recovered \nnon-integrated viral genomes using QIAprep Spin Miniprep Kit. We then performed two rounds of PCR to amplify the \nbarcoded spikes in the recovered lentivirus genomes, minimizing the number of PCR cycles to avoid strand-switching. \nLong-read circular consensus sequencing was performed on ampli\nfi\ned virus genomes using PacBio Sequel IIe machine. \nConsensus sequence for each variant was determined using at least 2 CCSs per barcode. Variant-barcode lookup table \nfor both biological KP.3.1.1 library replicates is at \nhttps://github.com/dms-vep/SARS-CoV-2_KP .3.1.1_spike_DMS/blob/main/results/variants/codon_variants.csv. On \naverage each variant had 1.25 and 1.27 mutations per spike for library-1 and library-2, respectively.  \nMeasurement of mutation e\nff\nects on cell entry e\nff\nect  \nKP.3.1.1 spike pseudotyped viruses were produced from cell-stored libraries as described previously (2). 150 million \nlibrary cells were plated into 5-layer fl\nasks (Corning Falcon 875cm² Rectangular Straight Neck Cell Culture Multi-Flask, \n12 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nCat. No. 353144) in the presence of 1 µg/ml of doxycycline to induce spike expression from TRE3G promoter in the \nlentivirus backbone. Next day cells were transfected with 50 µg of each lentiviral helper plasmid and during transfection \ncell media was replaced with fresh serum-free media (Opti-MEM supplemented with 0.1% heat-inactivated FBS, 0.3% \nbovine serum albumin, 100 µg/mL of calcium chloride, 100 U/mL penicillin, and 100 µg/mL streptomycin). Serum free \nmedia was used because it allows better virus concentration in protein columns as FBS tends to clog column \nfi\nlters. 48 \nhours after transfection cell supernatant was collected, puri\nfi\ned from cell debris and concentrated using protein columns. \nProtein column concentrated virus titers varied between 12-25 million transcription units per ml. VSV-G pseudotyped \nviruses were also produced in parallel to spike pseudotyped libraries, using protocol described in the section above. For \ncell entry e\nff\nect measurements both 3 million transcription units of spike pseudotyped libraries and 10 million transcription \nunits VSV-G pseudotyped libraries were used to infect medium-ACE2 (24) cells and 293T cells, respectively. For spike \npseudotyped library infections cells were plated in the presence of 2.5 µg/ml of amphotericin B (Sigma, Cat. No. A2942), \nwhich we have shown in the past increases virus titers (21). 15 hours after infection non-integrated viral genomes were  \nrecovered using QIAprep Spin Miniprep Kit and amplicon libraries were prepared for illumina sequencing as described \npreviously using dual indexing for each sample to avoid index hopping on certain sequencing platforms (21). Sequencing \nwas performed on NovaSeq X Plus and NextSeq 2000 platforms. \nMutation e\nff\nects on cell entry were calculated using log enrichment ratio: , 𝑙𝑜𝑔2 𝑛\n𝑣\n𝑝𝑜𝑠𝑡 / 𝑛\n𝑤𝑡\n𝑝𝑜𝑠𝑡\n⎡⎢\n⎣\n⎤\n⎥\n⎦/ 𝑛\n𝑣\n𝑝𝑟𝑒 / 𝑛\n𝑤𝑡\n𝑝𝑟𝑒 \n⎡\n⎢\n⎣\n⎤\n⎥\n⎦\n( )\nwhere is variant count post-infection (spike pseudotyped virus infection), is variant count pre infection (VSV-G 𝑛\n𝑣\n𝑝𝑜𝑠𝑡 𝑛\n𝑣\n𝑝𝑟𝑒 \npseudotyped virus infection) and  and  are unmutated variant counts post- and pre-infection. The multidms 𝑛\n𝑤𝑡\n𝑝𝑜𝑠𝑡 𝑛\n𝑤𝑡\n𝑝𝑟𝑒 \n(74) package was used to fi\nt global epistasis models (75) on variant e\nff\nect data to estimate the e\nff\nects of individual \nmutations from the full libraries of both singly and multiply mutated spike variants. The values reported here are the \nmedian across the measurements with all replicates of both libraries. \nMeasurement of mutation e\nff\nects on receptor binding \nTo measure how mutations to spike a\nff\nect ACE2 binding we used soluble monomeric ACE2. Monomeric ACE2 was \nproduced as described previously (2). First, we mixed 1.5 million transcription units of spike pseudotyped library virus per \nsample with RDPro pseudotyped virus at 1-2 % of total transcription units used. Use and production of RDPro \npseudotyped virus was described previously (2). RDPro is used in our experiments as a non-neutralizable standard to \nconvert sequencing counts to fractional neutralization of each variant at each ACE2 concentration as described \npreviously (2). The library virus was then mixed with increasing concentrations of soluble monomeric ACE2 and incubated \nat 37°C for 30 min. The ACE2 concentrations were selected such that they would cover most of the KP.3.1.1 spike \npseudotyped virus neutralization range in order to identify mutations that both increase (spike variants that are neutralized \nwell at low ACE2 concentrations) and decrease (spike variants that are neutralized at high ACE2 concentrations) ACE2 \nbinding; speci\nfi\nc concentrations used were 6, 13, 27, 54, and 115 µg/ml. After incubation, libraries were used to infect \nmedium-ACE2 cells in the presence of 2.5 µg/ml of amphotericin B and 15 h post infection non-integrated viral genomes \nwere recovered and prepared for Illumina sequencing as described previously (21). After converting the sequencing \ncounts to the fractional neutralization using the non-neutralized RDPro standard (2), we analyzed the data using a \nbiophysical model implemented in the polyclonal software package (https://github.com/jbloomlab/polyclonal )(76) to \ndetermine the e\nff\nect of each mutation on ACE2 neutralization, reporting the values such that positive e\nff\nects indicate \nimproved ACE2 binding (higher neutralization by soluble ACE2). We performed ACE2 binding experiments with both \nlibrary-1 and library-2 biological replicates. The values reported here are the median across both replicates. Mutations \ne\nff\nects on ACE2 binding are shown at https://dms-vep.org/SARS-CoV-2_KP .3.1.1_spike_DMS/receptor_binding.html. \n \nMeasurement of mutation e\nff\nects on serum and antibody neutralization \nBefore performing sera and antibody selection experiments with deep mutational scanning libraries we determined  their \npotency by performing pseudovirus neutralization assays on viruses pseudotyped with KP.3.1.1 spike. Pseudovirus \n13 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nneutralization assays were performed as described previously (36) and in Standard pseudovirus neutralization assays \nsection below. Before use, all sera were inactivated for 1 h at 56°C.  \nFor each sample 1.5 million transcription units of spike pseudotyped library virus were mixed with RDPro \npseudotyped virus at 1-2 % of total transcription units used. For each sera we performed selection at three \nconcentrations aiming to neutralize more than 60% of library variants in at least two of these concentrations. Our starting \nserum dilution was twice the IC99 value as determined by standard pseudovirus neutralization, which typically \nsigni\nfi\ncantly underestimates neutralization achieved for deep mutational scanning (perhaps due to di\nff\nering amounts of \nspike on the surface of pseudoviruses used in standard neutralization assay versus library virus, or depletion of antibody \nmolecules by the higher virion concentration in the library experiments). An example of neutralization achieved by di\nff\nerent \nserum concentration can be seen here https://dms-vep.org/SARS-CoV-2_KP .3.1.1_spike_DMS/notebooks/avg_escape_ \nantibody_escape_adult-1_pre_vaccination.html in the probability escape plots. Generally, serum escape probabilities > \n0.4 allow identi\nfi\ncation of mutations that a\nff\nect serum neutralization. Antibodies we used the following concentrations: \nBD55-1205 these concentrations were 0.73, 2.18, and 6.55 µg/ml, for SA55 0.32, 0.95, and 2.84 µg/ml, and for \nVYD222 100, 300, and 900 µg/ml. In standard pseudovirus neutralization assays all these concentrations were above \nIC99 value, but in deep mutational scanning data these ranged between IC50-IC99 for BD55-1205, IC5-IC75 for SA55 \nand IC94-IC99 for VYD222. After incubation, virus mixtures were used to infect medium-ACE2 cells in the presence of \n2.5 µg/ml of amphotericin B and 15 h post infection non-integrated viral genomes were recovered and prepared for \nillumina sequencing as described previously (21).  \n To determine mutations which a\nff\nect serum or antibody neutralization we used a biophysical model from \npolyclonal (v6.16) package (76), which is implemented in dms-vep-pipeline-3 (v3.27.0) \nhttps://github.com/dms-vep/dms-vep-pipeline-3/tree/main.  \nMean and individual sera escape plots and links to raw numeric escape values for each sera are at \nhttps://dms-vep.org/SARS-CoV-2_KP .3.1.1_spike_DMS/polyclonal_sera_escape.html. Interactive plots showing escape \nfor BD55-1205, SA55 and VYD22 antibodies are at \nhttps://dms-vep.org/SARS-CoV-2_KP .3.1.1_spike_DMS/antibody_escape.html. The latter link also contains interactive \nstructure visualizations showing deep mutational scanning measured escape in the context of RBD bound to each of the \nantibodies.  \nEstimate of mutation e\nff\nects on RBD up/down motion \n \nTo quantify a site’s e\nff\nect on RBD up/down motion we used the following formula: \n  𝑆𝑖𝑡𝑒 𝑒𝑓𝑓𝑒𝑐𝑡 𝑜𝑛 𝑅𝐵𝐷 𝑚𝑜𝑡𝑖𝑜𝑛 = 𝑅 𝑠 ×− 1 × 1\n𝑛𝑠 𝑖=1\n𝑛\n∑ 𝑒𝑠𝑐𝑎𝑝𝑒 𝑠,𝑖\n2\n ×  1\n𝑛𝑠 𝑖=1\n𝑛\n∑ 𝑏𝑖𝑛𝑑𝑖𝑛𝑔 𝑠,𝑖\n2\n \nwhere  is Pearson correlation between mutation e\nff\nects on serum escape (averaged across all sera) and ACE2 binding 𝑅\nfor site . Positive R values were set to zero and then R was multiplied by negative 1. The root mean square of mutation 𝑠\ne\nff\nects on serum escape is calculated as , where   is the measured serum escape e\nff\nect 1\n𝑛𝑠 𝑖=1\n𝑛\n∑ 𝑒𝑠𝑐𝑎𝑝𝑒 𝑠,𝑖\n2\n \n𝑒𝑠𝑐𝑎𝑝𝑒𝑠,𝑖\n(averaged across all sera) of mutation  at site , and  is the number of mutations measured at site , and 𝑛 𝑠 𝑛𝑠 𝑠\n is the root mean square of mutation e\nff\nects on ACE2 binding.  1\n𝑛𝑠 𝑖=1\n𝑛\n∑ 𝑏𝑖𝑛𝑑𝑖𝑛𝑔 𝑠,𝑖\n2\n \nComparison with prior XBB.1.5 spike deep mutational scanning \nPseudovirus based deep mutational scanning data for XBB.1.5 spike was published previously in Dadonaite et al (2024). \nThat dataset included two spike libraries: full spike deep mutational scanning library, where a subset of mutations was \nincluded throughout the spike protein, and RBD-only library, where all possible mutations were introduced only in the \nRBD. Fig. 3C compares ACE2 binding data for KP.3.1.1 deep mutational scanning libraries with full spike XBB.1.5 \n14 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nlibraries and Fig. 3D compares ACE2 binding data for KP.3.1.1 deep mutational scanning libraries and XBB.1.5 \nRBD-only libraries for sites 435 and 493. \nStandard pseudovirus neutralization assays  \nDesired mutations were cloned into KP.3.1.1 spike expression plasmid https://github.com/dms-vep/SARS-CoV-2_ \nKP .3.1.1_spike_DMS/blob/main/KP311_validation_notebooks/plasmid_maps/HDM_KP .3.1.1.gb and sequence was \ncon\nfi\nrmed using whole plasmid sequencing. Spike pseudotyped lentiviruses were rescued by transfecting 293T cells with \nspike expression plasmids, Gag/Pol (BEI: NR-52517) helper plasmid and  pHAGE6_Luciferase_IRES_ZsGreen backbone. \n48 hours post transfection virus-containing cell supernatants were collected and titrated. Neutralization assays were \nperformed as described in Crawford et al. (2020) using medium-ACE2 cells (24) in the presence of 2.5 µg/ml of \namphotericin B. For all neutralization assays starting dilution was 0.05 and we performed eight 3-fold serial dilutions. \nFraction infectivity at each dilution was determined relative to serum free controls and neutcurve (V2.1.0) package (77) \nwas used to fi\nt Hill curves to fraction infectivity data.  \nAntibody Production \nAntibodies were ordered from Genscript Biotech using published variable sequences (12,41,42,78). Variable sequences \nand complete expressed polypeptide sequences are speci\nfi\ned in Table S2. These sequences were codon-optimized, \ncloned into expression vectors, and expressed in Chinese hamster ovary-derived cells. Heavy chain variable sequences \nwere cloned into a human IgG1 backbone. The light chain variable sequences for BD55-1205 and SA55 were cloned \ninto a human kappa light chain backbone; VYD222 was cloned into a human lambda light chain backbone.  \nCells \n293T, 293T-rtTA, medium-ACE2 and cell-stored library cells were all grown in D10 media (Dulbecco’s Modi\nfi\ned Eagle \nMedium with 10% heat-inactivated fetal bovine serum, 2 mM l-glutamine, 100 U/mL penicillin, and 100 μg/mL \nstreptomycin). For deep mutational scanning library and 293T-rtTA cells tetracycline-free FBS was used. Medium-ACE2 \ncells were grown in the presence of 2 µg/ml doxycycline, which induced ACE2 expression in these cells. \nEthics statement \nPre- and post-vaccination or infection sera were collected with informed consent from participants in the prospective \nlongitudinal Hospitalized or Ambulatory Adults with Respiratory Viral Infections (HAARVI) study. The study was approved \nby University of Washington Institutional Review Board (#STUDY00000959). \n \n15 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nFigures \n \n \nFigure 1. Measurement of di\nff\nerent spike phenotypes using KP.3.1.1 spike deep \nmutational scanning \nA. We measured the effects of mutations in the KP.3.1.1 spike on pseudovirus entry into 293T cells \nexpressing ACE2, binding to ACE2 receptor, RBD up/down motion, neutralization by human sera, \n16 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nand neutralization by several key monoclonal antibodies. B. Spike amino-acid mutations and \ndeletions in the KP.3.1.1 spike used in our deep mutational scanning and other key \nJN.1-descendant lineages relative to the early Wuhan-Hu-1 strain. Site labels indicate the \namino-acid identity and residue number in the Wuhan-Hu-1 strain. Sites that differ among JN.1 and \nits descendant strains are bolded; non-bolded sites have fixed mutations relative to Wuhan-Hu-1 \nshared among all the lineages shown. When a variant has the same identity at a site as \nWuhan-Hu-1, this is indicated with empty white space. Insertions are not shown; all JN.1 \ndescendant lineages have an MPLF amino-acid insertion at position 16.   \n17 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\n \nFigure 2. E\nff\nects of mutations to the KP.3.1.1 spike on pseudovirus entry in \nACE2-expressing cells \nA. Effects of mutations in spike on entry in 293T cells expressing a medium amount of ACE2 (24). \nEffects greater than zero (blue) indicate a mutation improves cell entry while e ffects less than zero \n18 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\n(orange) indicate a mutation impairs cell entry. X indicates the wild-type amino acid in KP.3.1.1. \nLight grey indicates mutations for which e ffects were not measured in our libraries; note that our \nlibrary design excluded most mutations expected to be highly deleterious from all regions of the \nspike except for the RBD. Due to space constraints this figure shows only the NTD and RBD; see \nhttps://dms-vep.org/SARS-CoV-2_KP .3.1.1_spike_DMS/cell_entry.html for an interactive heatmap \nthat shows mutations across the full spike. B. Effects on cell entry for some key recent mutations in \nthe KP.3.1.1 versus XBB.1.5 spikes. The e ffects in the KP.3.1.1 spike are from the current study, \nthe effects in the XBB.1.5 spike were published previously (2).  \n \n \n \n19 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\n \nFigure 3. E\nff\nects of mutations to the KP.3.1.1 spike on ACE2 binding \n20 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nA. E ffects of mutations in spike on ACE2 binding. E ffects greater than zero (blue) indicate a \nmutation improves ACE2 binding while e ffects less than zero (orange) indicate a mutation \ndecreases ACE2 binding. X indicates wild-type amino acid in KP.3.1.1. Dark grey indicates \nmutations that were present in our libraries but too deleterious for cell entry to measure an effect on \nACE2 binding; light grey indicates mutations for which e ffects were not measured in our libraries. \nDue to space constraints this figure shows only the NTD and RBD; see \nhttps://dms-vep.org/SARS-CoV-2_KP .3.1.1_spike_DMS/receptor_binding.html for an interactive \nheatmap that shows e ffects of mutations across the full spike, as well as interactive versions of \nother panels of this figure. B. Correlation between ACE2 binding measurements for the two \nindependent deep mutational scanning library replicates faceted by proximity to ACE2. ACE2 \nproximal sites are de fined as those within 15 Å distance from ACE2 in ACE2-bound RBD structure \n(PDB: 6M0J). C. Correlation between the e ffects of RBD mutations on ACE2 binding measured for \nthe KP.3.1.1 spike in the current study and the XBB.1.5 spike in prior work (2). D. Mutation e ffects \non ACE2 binding at sites 435 and 493 measured in XBB.1.5 versus KP.3.1.1 deep mutational \nscanning libraries. Amino-acids are coloured by their chemical properties. \n21 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\n \nFigure 4. E\nff\nects of mutations to the KP.3.1.1 spike on serum neutralization \nA. Total neutralization escape by all measured mutations at each site in spike averaged across all \nseven pre- or post-vaccination or infection sera. For more extensive interactive versions of the plots \n22 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nin this figure including heatmaps with per-mutation effects,  see https://dms-vep.org/SARS-CoV-2 \n_KP .3.1.1_spike_DMS/polyclonal_sera_escape.html. B. Comparison between escape at RBD sites \npre- and post-vaccination or infection for each of the seven individual sera. Note that this plot only \nshows positive escape values (mutations that reduce neutralization), and not mutations that \nincrease neutralization (negative escape), although the interactive plots linked in this legend have \noptions to view the negative escape.  \n23 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\n \nFigure 5. Sites where mutations a\nff\nect RBD up/down conformation \nA. Correlation between the measured e ffects of each mutation on ACE2 binding and serum \nantibody escape, faceted by proximity of the site to ACE2. B. Experimentally estimated effect of \nmutations at each site on RBD up/down conformation. The larger the value, the greater e ffect \nmutations at that site have on RBD up/down conformation, although individual mutations at each \nsite may have opposing e ffects. Sites within the receptor-binding motif (RBM) are colored red, and \nall other sites are blue. See https://dms-vep.org/SARS-CoV-2_KP .3.1.1_spike_DMS/RBD_ \nmovement.html for an interactive version of this plot. The e ffect of each site on RBD up/down \nconformation is estimated from the deep mutational scanning by calculating correlation (Pearson R) \nbetween serum neutralization escape and ACE2 binding for all mutations at each site, then \nmultiplying that correlation by minus one and weighting it by the root-mean-square (RMS) e ffect of \nall mutations at the site on ACE2 binding and the RMS e ffect of all mutations at the site on serum \nneutralization escape. Sites with positive correlation had the e ffect floored to zero. This metric \ncaptures the fact that mutations at sites that a ffect RBD up/down conformation have opposing \neffects on ACE2 binding and serum neutralization escape. Only sites where binding and \nneutralization effects could be measured for at least three mutations are shown.  \n \n24 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\n \n \nFigure 6. Mutations that reduce neutralization by monoclonal antibodies \nBD55-1205, SA55 and VYD222  \nA. Mutations that reduce neutralization by the BD55-1205 antibody. The line plot on the left shows \nthe total escape caused by all mutations at each site in spike. The logo plot in the middle shows \nescape caused by each mutation at key sites; letter heights indicate escape caused by each \nmutation, and mutations are colored by their effect on ACE2 binding. The structure at right shows a \nsurface representation of the RBD bound by BD55-1205, with the RBD colored by the total escape \n25 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nat each site (PDB ID: 8XE9). B-C. Same as A but for SA55 and VYD222, respectively. For SA55, \nthe structure is PDB ID 7Y0W For VYD222, the structure is PDB ID 7U2D, which shows ADG20, \nwhich is the parent antibody from which VYD222 is derived (44). Only positive escape values \n(mutations that reduce neutralization) are shown. For a more detailed interactive plot showing \nmutation-level escape across the spike for all three antibodies, see \nhttps://dms-vep.org/SARS-CoV-2_KP .3.1.1_spike_DMS/antibody_escape.html.  \n26 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nSupplementary figures \n \nSupplementary Figure 1. Design of KP.3.1.1 spike deep mutational scanning \nlibraries \nA. Method for producing genotype-phenotype linked pseudovirus-based deep mutational scanning \nlibraries as applied to the KP.3.1.1 spike. 293T cells are transfected with lentivirus helper plasmids, \nlentiviral backbone plasmids encoding the barcoded KP.3.1.1 spike variant library, and VSV-G \nexpression plasmid to produce VSV-G pseudotyped viruses. The viruses are then used to infect \n27 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\n293T cells expressing reverse tetracycline transactivator (rtTA) at low multiplicity of infection (MOI, \n<0.01) so that only a single virus genome integrates in any given cell. Cells are then selected for \nsuccessful transduction using puromycin. From selected cells, genotype-phenotype linked virus \nlibraries are made by inducing spike expression using doxycycline and transfecting lentivirus helper \nplasmids. To quantify the presence of non-functional as well as functional spike variants present in \nthe libraries, we also rescue VSV-G pseudotyped viruses from the same library cells by transfecting \nlentivirus helper plasmids and VSV-G expression plasmids. B. Number of targeted and successfully \nincluded mutations in each of the two independent libraries. Note that our library design primarily \ntargeted mutations expected to be functionally tolerated, see text and Methods for details. C. \nDistribution of mutations per spike variant for each library. D. Correlation between the cell entry \ne\nffects for all high-con fidence measured mutations in both of the two independent libraries. \nThroughout the paper we show the average measurement across both libraries unless otherwise \nindicated.  \n28 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\n \n \nSupplementary Figure 2. E\nff\nects of mutations on ACE2 binding \nA. To measure e ffects of mutations to spike protein on ACE2 binding, deep mutational scanning \nlibraries are incubated with monomeric soluble human ACE2 at multiple concentrations followed by \ninfection of 293T-ACE2 cells expressing medium levels of ACE2. Library variants with mutations \nthat increase ACE2 are better neutralized by soluble ACE2 compared to variants with mutations \nthat decrease ACE2 binding. B. Correlation between mutation e ffects on ACE2 binding and cell \nentry; note it is only possible for our method to measure ACE2 binding for mutations that maintain \nat least modest levels of cell entry. C. SARS-CoV-2 spike structure with one RBD up in contact \nwith ACE2 (PDB: 8IOU). Spheres show ACE2 distal RBD sites with strong e ffects on ACE2 binding \nas highlighted in Fig. 3B.  \n \n \n29 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\n \nSupplementary Figure 3. Neutralization of KP.3.1.1 pseudovirus by serum from the \nsame individual collected pre- and post-exposure to a JN.1 descendant spike \nA. Neutralization curves for the sera from seven individuals analyzed in this study. Each plot shows \nsera from the same individual pre- and post-exposure to JN.1-descendant spike. Curves were \n30 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nmeasured using standard neutralization assays with KP.3.1.1 spike pseudotyped lentiviral particles. \nB. Neutralizing titers against KP.3.1.1 pseudovirus for sera pre- and post-exposure to \nJN.1-descendant spike calculated from the neutralization curves in A. C. Correlation between deep \nmutational scanning measured sera escape scores for pre- and post- JN.1-descendant spike \nexposed sera for each individual. Each point is a di fferent mutation and shows the measured effect \nof that mutation on escape from each serum.  \n31 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\n \n \nSupplementary Figure 4. E\nff\nects of mutations to KP.3.1.1 spike on serum \nneutralization as measured by pseudovirus neutralization assay \nA. Correlation between deep mutational scanning measured escape scores and IC50 values \nmeasured using a standard pseudovirus neutralization assay for various KP.3.1.1 spike mutants. B. \n32 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nFold change in IC50 values for di fferent KP.3.1.1 spike mutants relative to the unmutated KP.3.1.1 \nspike for pre- and post-vaccination or infection sera, as measured using a standard pseudovirus \nneutralization assay. All mutations were measured for four sera except for V570W which was only \nmeasured for two sera.  \n33 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\n \nSupplementary Figure 5. Escape for BD55-1205, SA55 and VYD222 antibodies \nmeasured by yeast-based RBD versus pseudovirus-based full-spike deep \nmutational scanning \n34 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nA. Top logoplot shows e ffects of mutations at key sites on BD55-1205 antibody binding as \nmeasured using yeast-based deep mutational scanning of JN.1 RBD in previously published work \nby Jian et al (6). The height of the letter indicates binding escape for each mutation. The bottom \nlogoplot shows mutations e ffects on neutralization by BD55-1205 as measured by KP.3.1.1 \nfull-spile deep mutational scanning and is the same as in Fig. 6, with mutations colored according \nto their e ffect on ACE2 binding in the KP.3.1.1 full-spike pseudovirus deep mutational scanning. \nSites where the parental amino acid di ffers between JN.1 and KP.3.1.1 backgrounds are labeled in \nboth logoplots; sites labeled in just the bottom logoplot have the same parental amino acid in both \nJN.1 and KP.3.1.1. B-C. Same as A but for SA55 and VYD222 antibodies, respectively. No \nmeasurement was reported for site 502 for BD55-1205, and sites 502 and 505 for SA55 in the \nRBD-only deep mutational scanning data. For BD55-1205 no measurements are available for sites \n480 and 488 in full spike deep mutational scanning because all mutations at those sites are highly \ndeleterious for cell entry.  \n \n35 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\n \nSupplementary Figure 6. Cloning of KP.3.1.1 deep mutational scanning library \nA. To produce plasmid library for deep mutational scanning using Golden Gate assembly, the \nKP.3.1.1 spike sequence was divided into 17 overlapping tiles. B. For each tile we computationally \ndesigned a pool of oligos containing all desired mutations. C. Designed oligos were ordered as a \nsingle-stranded DNA (ssDNA) oligo pool from which oligos belonging to each of the 17 tiles were \namplified with primers containing the BsmBI restriction site. Unmutated spike sequences flanking \neach tile were also ampli fied. D. Golden Gate assembly was performed to assemble each tile pool \nand flanking spike sequences into a shuttle vector. E. Assembled spike sequences were amplified \nand barcoded in the same PCR reaction. Ampli fied and barcoded spike sequences were pooled \n36 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nequimolarly to make one library. F. The barcoded spike pool was cloned into a lentiviral vector using \na HiFi reaction.  \n37 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nSupplementary Tables \nSupplementary Table 1. Information about sera used in this study \nWe used pre- and post-exposure sera from seven adults. The table indicates the last exposure (by \nvaccination or infection) and the days after this exposure that the “post-exposure” sera was \ncollected. The “pre-exposure” serum from each individual was the last blood drawn prior to this \nfinal exposure, although individuals had di fferent number of  vaccinations or infections before this \n“pre-exposure” serum was collected. The table also indicates the last exposure before the \n“pre-exposure” serum collection. In all cases, the “post-exposure” serum was after a vaccination \nwith the KP.2 spike or an infection in May-November of 2024, when JN.1-descendant variants \ndominated in Washington state where all the sera were collected (79). \n \n \nSupplementary Table 2. Antibody sequences \nVariable chain sequences for BD55-1205, SA55 and VYD222 antibodies. The complete expressed \npolypeptide sequences, including the human IgG1, lambda, or kappa constant sequences are \nprovided. Heavy (HC) and light chains (LC) were expressed with murine Ig heavy chain V region \n102 as an N-terminal export signal sequence.  \n38 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nReferences \n1. Bloom JD, Neher RA. Fitness effects of mutations to SARS-CoV-2 proteins. Virus Evol. 2023 \nJan 1;9(2):vead055.  \n2. Dadonaite B, Brown J, McMahon TE, Farrell AG, Figgins MD, Asarnow D, et al. Spike deep \nmutational scanning helps predict success of SARS-CoV-2 clades. Nature. 2024 \nJul;631(8021):617–26.  \n3. Lamb KD, Luka MM, Saathoff M, Orton RJ, Phan MVT, Cotten M, et al. Mutational signature \ndynamics indicate SARS-CoV-2’s evolutionary capacity is driven by host antiviral molecules. \nPLOS Comput Biol. 2024 Jan 25;20(1):e1011795.  \n4. Greaney AJ, Starr TN, Bloom JD. An antibody-escape estimator for mutations to the \nSARS-CoV-2 receptor-binding domain. Virus Evol. 2022 Jan 1;8(1):veac021.  \n5. Greaney AJ, Loes AN, Gentles LE, Crawford KHD, Starr TN, Malone KD, et al. Antibodies \nelicited by mRNA-1273 vaccination bind more broadly to the receptor binding domain than do \nthose from SARS-CoV-2 infection. Sci Transl Med. 2021 Jun 30;13(600):eabi9915.  \n6. Jian F, Wang J, Yisimayi A, Song W, Xu Y, Chen X, et al. Evolving antibody response to \nSARS-CoV-2 antigenic shift from XBB to JN.1. Nature. 2024 Nov 7;1–3.  \n7. Feng L, Sun Z, Zhang Y, Jian F, Yang S, Xia K, et al. Structural and molecular basis of the \nepistasis e\nffect in enhanced affinity between SARS-CoV-2 KP.3 and ACE2. Cell Discov. 2024 \nNov 30;10(1):123.  \n8. Starr TN, Greaney AJ, Hannon WW, Loes AN, Hauser K, Dillen JR, et al. Shifting mutational \nconstraints in the SARS-CoV-2 receptor-binding domain during viral evolution. Science. 2022 \nJul 22;377(6604):420–4.  \n9. Starr TN, Greaney AJ, Hilton SK, Ellis D, Crawford KHD, Dingens AS, et al. Deep Mutational \nScanning of SARS-CoV-2 Receptor Binding Domain Reveals Constraints on Folding and ACE2 \nBinding. Cell. 2020 Sep 3;182(5):1295-1310.e20.  \n10. Ozono S, Zhang Y, Ode H, Sano K, Tan TS, Imai K, et al. SARS-CoV-2 D614G spike \nmutation increases entry efficiency with enhanced ACE2-binding affinity. Nat Commun. 2021 \nFeb 8;12(1):848.  \n11. Khatri R, Siddqui G, Sadhu S, Maithil V, Vishwakarma P, Lohiya B, et al. Intrinsic D614G \nand P681R/H mutations in SARS-CoV-2 VoCs Alpha, Delta, Omicron and viruses with D614G \nplus key signature mutations in spike protein alters fusogenicity and infectivity. Med Microbiol \nImmunol (Berl). 2023 Feb 1;212(1):103–22.  \n12. Jian F, Wec AZ, Feng L, Yu Y, Wang L, Wang P, et al. Viral evolution prediction identifies \nbroadly neutralizing antibodies to existing and prospective SARS-CoV-2 variants. Nat Microbiol. \n2025 Jun 10;1–15.  \n39 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\n13. Lei R, Qing E, Odle A, Yuan M, Gunawardene CD, Tan TJC, et al. Functional and antigenic \ncharacterization of SARS-CoV-2 spike fusion peptide by deep mutational scanning. Nat \nCommun. 2024 May 14;15:4056.  \n14. Tan TJC, Mou Z, Lei R, Ouyang WO, Yuan M, Song G, et al. High-throughput identification \nof prefusion-stabilizing mutations in SARS-CoV-2 spike. Nat Commun. 2023 Apr 10;14(1):2003.  \n15. Jian F, Feng L, Yang S, Yu Y, Wang L, Song W, et al. Convergent evolution of SARS-CoV-2 \nXBB lineages on receptor-binding domain 455-456 synergistically enhances antibody evasion \nand ACE2 binding [Internet]. bioRxiv; 2023 [cited 2023 Oct 31]. p. 2023.08.30.555211. \nAvailable from: https://www.biorxiv.org/content/10.1101/2023.08.30.555211v2  \n16. Taylor AL, Starr TN. Deep mutational scanning of SARS-CoV-2 Omicron BA.2.86 and \nepistatic emergence of the KP.3 variant. Virus Evol. 2024 Oct 17;10(1):veae067.  \n17. Meijers M, Ruchnewitz D, Eberhardt J, Łuksza M, Lässig M. Population immunity predicts \nevolutionary trajectories of SARS-CoV-2. Cell. 2023 Nov 9;186(23):5151-5164.e13.  \n18. Suntronwong N, Assawakosri S, Klinfueng S, Duangchinda T, Chantima W, Pakchotanon P, \net al. Age associated SARS-CoV-2 immune responses provide insights into population immunity \nover four years since the COVID-19 pandemic. Sci Rep. 2025 Jul 2;15(1):23183.  \n19. Raharinirina NA, Gubela N, Börnigen D, Smith MR, Oh DY, Budt M, et al. SARS-CoV-2 \nevolution on a dynamic immune landscape. Nature. 2025 Mar;639(8053):196–204.  \n20. Dadonaite B, Burrell AR, Logue J, Chu HY, Payne DC, Haslam DB, et al. SARS-CoV-2 \nneutralizing antibody specificities differ dramatically between recently infected infants and \nimmune-imprinted individuals. J Virol. 2025 Mar 25;99(4):e00109-25.  \n21. Dadonaite B, Crawford KHD, Radford CE, Farrell AG, Yu TC, Hannon WW, et al. A \npseudovirus system enables deep mutational scanning of the full SARS-CoV-2 spike. Cell. 2023 \nMar 16;186(6):1263-1278.e20.  \n22. Roemer, Cornelius, Neher, Richard. GitHub. 2024 [cited 2025 Jul 2]. \nSARS-CoV-2_variant-reports_2024-12-20. Available from: \nhttps://github.com/neherlab/SARS-CoV-2_variant-reports/blob/b85f7caeb1503ad2a803d3e38\n568a4e3c767216c/reports/variant_report_2024-12-20.md  \n23. World Health Organisation. Statement on the antigen composition of COVID-19 vaccines \n[Internet]. 2025 [cited 2025 Jul 2]. Available from: \nhttps://www.who.int/news/item/15-05-2025-statement-on-the-antigen-composition-of-covid-1\n9-vaccines  \n24. Farrell AG, Dadonaite B, Greaney AJ, Eguia R, Loes AN, Franko NM, et al. \nReceptor-Binding Domain (RBD) Antibodies Contribute More to SARS-CoV-2 Neutralization \nWhen Target Cells Express High Levels of ACE2. Viruses. 2022 Sep;14(9):2061.  \n25. Cantoni D, Murray MJ, Kalemera MD, Dicken SJ, Stejskal L, Brown G, et al. Evolutionary \n40 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nremodelling of N‐terminal domain loops fine‐tunes SARS‐CoV‐2 spike. EMBO Rep. 2022 Oct \n6;23(10):e54322.  \n26. Cao Y, Jian F, Wang J, Yu Y, Song W, Yisimayi A, et al. Imprinted SARS-CoV-2 humoral \nimmunity induces convergent Omicron RBD evolution. Nature. 2023 Feb;614(7948):521–9.  \n27. Wang Q, Iketani S, Li Z, Guo Y, Yeh AY, Liu M, et al. Antigenic characterization of the \nSARS-CoV-2 Omicron subvariant BA.2.75. Cell Host Microbe. 2022 Nov \n9;30(11):1512-1517.e4.  \n28. Wang Q, Nair MS, Anang S, Zhang S, Nguyen H, Huang Y, et al. Functional differences \namong the spike glycoproteins of multiple emerging severe acute respiratory syndrome \ncoronavirus 2 variants of concern. iScience. 2021 Nov 19;24(11):103393.  \n29. Walls AC, Xiong X, Park YJ, Tortorici MA, Snijder J, Quispe J, et al. Unexpected Receptor \nFunctional Mimicry Elucidates Activation of Coronavirus Fusion. Cell. 2019 Feb \n21;176(5):1026-1039.e15.  \n30. Tse AL, Acreman CM, Ricardo-Lax I, Berrigan J, Lasso G, Balogun T, et al. Distinct \npathways for evolution of enhanced receptor binding and cell entry in SARS-like bat \ncoronaviruses. PLOS Pathog. 2024 Nov 15;20(11):e1012704.  \n31. Uğurlu H, Salminen P, Yuan M, Fagerlund R, Mäkelä AR, Žusinaite E, et al. Altered spike \ndynamics drives immune evasion of SARS-CoV-2 [Internet]. bioRxiv; 2024 [cited 2025 Jul 19]. p. \n2024.12.03.626570. Available from: \nhttps://www.biorxiv.org/content/10.1101/2024.12.03.626570v1  \n32. Taylor AL, Starr TN. Deep mutational scans of XBB.1.5 and BQ.1.1 reveal ongoing epistatic \ndrift during SARS-CoV-2 evolution [Internet]. bioRxiv; 2023 [cited 2023 Sep 30]. p. \n2023.09.11.557279. Available from: \nhttps://www.biorxiv.org/content/10.1101/2023.09.11.557279v1  \n33. Wang Q, Mellis IA, Ho J, Bowen A, Kowalski-Dobson T, Valdez R, et al. Recurrent \nSARS-CoV-2 spike mutations confer growth advantages to select JN.1 sublineages. Emerg \nMicrobes Infect. 13(1):2402880.  \n34. Yajima H, Anraku Y, Kaku Y, Kimura KT, Plianchaisuk A, Okumura K, et al. Structural basis \nfor receptor-binding domain mobility of the spike in SARS-CoV-2 BA.2.86 and JN.1. Nat \nCommun. 2024 Oct 7;15(1):8574.  \n35. Wang Q, Guo Y, Mellis IA, Wu M, Mohri H, Gherasim C, et al. Antibody evasiveness of \nSARS-CoV-2 subvariants KP.3.1.1 and XEC. Cell Rep. 2025 Apr 22;44(4):115543.  \n36. Crawford KHD, Eguia R, Dingens AS, Loes AN, Malone KD, Wolf CR, et al. Protocol and \nReagents for Pseudotyping Lentiviral Particles with SARS-CoV-2 Spike Protein for Neutralization \nAssays. Viruses. 2020 May 6;12(5):513.  \n37. Hodcroft EB, Zuber M, Nadeau S, Vaughan TG, Crawford KHD, Althaus CL, et al. Spread \n41 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nof a SARS-CoV-2 variant through Europe in the summer of 2020. Nature. 2021 \nJul;595(7869):707–12.  \n38. Zhao Z, Zhou J, Tian M, Huang M, Liu S, Xie Y, et al. Omicron SARS-CoV-2 mutations \nstabilize spike up-RBD conformation and lead to a non-RBM-binding monoclonal antibody \nescape. Nat Commun. 2022 Aug 24;13(1):4958.  \n39. Yang H, Guo H, Wang A, Cao L, Fan Q, Jiang J, et al. Structural basis for the evolution and \nantibody evasion of SARS-CoV-2 BA.2.86 and JN.1 subvariants. Nat Commun. 2024 Sep \n4;15(1):7715.  \n40. Zhang QE, Lindenberger J, Parsons RJ, Thakur B, Parks R, Park CS, et al. SARS-CoV-2 \nOmicron XBB lineage spike structures, conformations, antigenicity, and receptor recognition. \nMol Cell. 2024 Jul 25;84(14):2747-2764.e7.  \n41. Cao Y, Jian F, Zhang Z, Yisimayi A, Hao X, Bao L, et al. Rational identification of potent and \nbroad sarbecovirus-neutralizing antibody cocktails from SARS convalescents. Cell Rep. 2022 \nDec 20;41(12):111845.  \n42. Ison MG, Popejoy M, Evgeniev N, Tzekova M, Mahoney K, Betancourt N, et al. Efficacy and \nSafety of Adintrevimab (ADG20) for the Treatment of High-Risk Ambulatory Patients With Mild or \nModerate Coronavirus Disease 2019: Results From a Phase 2/3, Randomized, \nPlacebo-Controlled Trial (STAMP) Conducted During Delta Predominance and Early Emergence \nof Omicron. Open Forum Infect Dis. 2023 May 24;10(6):ofad279.  \n43. SINOVAC Reports on the Clinical Trial Progress of its Anti-COVID-19 Antibody SA55 \nInjection-SINOVAC: Supply Vaccines to Eliminate Human Diseases [Internet]. [cited 2025 Jul 25]. \nAvailable from: \nhttps://www.sinovac.com/en-us/news/SINOVAC_Reports_on_the_Clinical_Trial_Progress_of_its\n_Anti_COVID_19_Antibody_SA55_Injection  \n44. Invivyd. Invivyd Announces FDA Authorization for Emergency Use of PEMGARDATM \n(Formerly VYD222) for Pre-exposure Prophylaxis (PrEP) of COVID-19 - Invivyd [Internet]. [cited \n2025 Jul 23]. Available from: \nhttps://investors.invivyd.com/news-releases/news-release-details/invivyd-announces-fda-authori\nzation-emergency-use-pemgardatm/  \n45. Cao Y, Wang J, Jian F, Xiao T, Song W, Yisimayi A, et al. Omicron escapes the majority of \nexisting SARS-CoV-2 neutralizing antibodies. Nature. 2022 Feb;602(7898):657–63.  \n46. Rappazzo CG, Tse LV, Kaku CI, Wrapp D, Sakharkar M, Huang D, et al. Broad and potent \nactivity against SARS-like viruses by an engineered human monoclonal antibody. Science. 2021 \nFeb 19;371(6531):823–9.  \n47. Wang Q, Guo Y, Ho J, Ho DD. Pemivibart is less active against recent SARS-CoV-2 JN.1 \nsublineages [Internet]. bioRxiv; 2024 [cited 2025 Jul 23]. p. 2024.08.12.607496. Available from: \nhttps://www.biorxiv.org/content/10.1101/2024.08.12.607496v2  \n42 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\n48. Powers C, Williams B, Kreher A, Gao F, West B, Chupp D, et al. Neutralization of recent \nSARS-CoV-2 variants by genetically and structurally related mAbs of the pemivibart lineage \n[Internet]. bioRxiv; 2024 [cited 2025 Jul 23]. p. 2024.11.11.623127. Available from: \nhttps://www.biorxiv.org/content/10.1101/2024.11.11.623127v1  \n49. Liu L, Iketani S, Guo Y, Chan JFW, Wang M, Liu L, et al. Striking antibody evasion \nmanifested by the Omicron variant of SARS-CoV-2. Nature. 2022 Feb;602(7898):676–81.  \n50. Tse AL, Lasso G, McLellan JS, Chandran K, Miller EH. Bat sarbecovirus WIV1-CoV bears \nan adaptive mutation that alters spike dynamics and enhances ACE2 binding [Internet]. bioRxiv; \n2025 [cited 2025 Aug 8]. p. 2025.04.14.648681. Available from: \nhttps://www.biorxiv.org/content/10.1101/2025.04.14.648681v1  \n51. Li Q, Cai X, Li X, Zhang Y, Li R, Kang Z, et al. Structural and functional constraints on spike \nactivation and host protease utilization limit cell entry of SARS-CoV-2-related bat coronaviruses. \nJ Virol. 2025 Jul 24;0(0):e01007-25.  \n52. Greaney AJ, Loes AN, Crawford KHD, Starr TN, Malone KD, Chu HY, et al. Comprehensive \nmapping of mutations in the SARS-CoV-2 receptor-binding domain that affect recognition by \npolyclonal human plasma antibodies. Cell Host Microbe. 2021 Mar 10;29(3):463-476.e6.  \n53. Piccoli L, Park YJ, Tortorici MA, Czudnochowski N, Walls AC, Beltramello M, et al. Mapping \nNeutralizing and Immunodominant Sites on the SARS-CoV-2 Spike Receptor-Binding Domain \nby Structure-Guided High-Resolution Serology. Cell. 2020 Nov 12;183(4):1024-1042.e21.  \n54. Premkumar L, Segovia-Chumbez B, Jadi R, Martinez DR, Raut R, Markmann AJ, et al. The \nreceptor-binding domain of the viral spike protein is an immunodominant and highly specific \ntarget of antibodies in SARS-CoV-2 patients. Sci Immunol. 2020 Jun 11;5(48):eabc8413.  \n55. Liang CY, Raju S, Liu Z, Li Y, Arunkumar GA, Case JB, et al. Imprinting of serum \nneutralizing antibodies by Wuhan-1 mRNA vaccines. Nature. 2024 Jun;630(8018):950–60.  \n56. Kaku CI, Starr TN, Zhou P, Dugan HL, Khalifé P, Song G, et al. Evolution of antibody \nimmunity following Omicron BA.1 breakthrough infection. Nat Commun. 2023 May \n12;14(1):2751.  \n57. Addetia A, Piccoli L, Case JB, Park YJ, Beltramello M, Guarino B, et al. Neutralization, \neffector function and immune imprinting of Omicron variants. Nature. 2023 \nSep;621(7979):592–601.  \n58. Yisimayi A, Song W, Wang J, Jian F, Yu Y, Chen X, et al. Repeated Omicron exposures \noverride ancestral SARS-CoV-2 immune imprinting. Nature. 2024 Jan;625(7993):148–56.  \n59. Schiepers A, van ’t Wout MFL, Greaney AJ, Zang T, Muramatsu H, Lin PJC, et al. Molecular \nfate-mapping of serum antibody responses to repeat immunization. Nature. 2023 \nMar;615(7952):482–9.  \n60. Roemer C, Neher RA. GitHub. 2025 [cited 2025 Aug 7]. SARS-CoV-2 variant reports. \n43 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nAvailable from: \nhttps://github.com/neherlab/SARS-CoV-2_variant-reports/blob/main/reports/variant_report_202\n5-04-23.md  \n61. Barrat-Charlaix P, Neher RA. Eco-evolutionary dynamics of adapting pathogens and host \nimmunity. eLife [Internet]. 2024 Oct 18 [cited 2025 Aug 8];13. Available from: \nhttps://elifesciences.org/reviewed-preprints/97350  \n62. Khare S, Gurry C, Freitas L, Schultz MB, Bach G, Diallo A, et al. GISAID’s role in pandemic \nresponse. China CDC Wkly. 2021;3(49):1049.  \n63. Turakhia Y, Thornlow B, Hinrichs AS, De Maio N, Gozashti L, Lanfear R, et al. Ultrafast \nSample placement on Existing tRees (UShER) enables real-time phylogenetics for the \nSARS-CoV-2 pandemic. Nat Genet. 2021 Jun;53(6):809–16.  \n64. O’Toole Á, Pybus OG, Abram ME, Kelly EJ, Rambaut A. Pango lineage designation and \nassignment using SARS-CoV-2 spike gene nucleotide sequences. BMC Genomics. 2022 Feb \n11;23(1):121.  \n65. Hanning KR, Walker EJ, Beijerling K, Irvine EB, Steel JJ, Kelton W. Simple high-throughput \nencoding of deep mutational scanning libraries by oligo-based Golden Gate assembly [Internet]. \nbioRxiv; 2025 [cited 2025 Aug 12]. p. 2025.07.16.665225. Available from: \nhttps://www.biorxiv.org/content/10.1101/2025.07.16.665225v1  \n66. Jann J, Gagnon-Arsenault I, Pageau A, Dubé AK, Fijarczyk A, Durand R, et al. Making deep \nmutational scanning accessible: a cost-efficient approach to construct barcoded libraries for \ngenes of any length [Internet]. bioRxiv; 2025 [cited 2025 Aug 12]. p. 2025.05.29.656836. \nAvailable from: https://www.biorxiv.org/content/10.1101/2025.05.29.656836v1  \n67. Álvarez-Rodríguez B, Velandia-Álvarez S, Toft C, Geller R. Mapping the mutational \nlandscape of a full viral proteome reveals distinct profiles of mutation tolerability [Internet]. \nbioRxiv; 2024 [cited 2025 Aug 12]. p. 2024.03.07.583990. Available from: \nhttps://www.biorxiv.org/content/10.1101/2024.03.07.583990v2  \n68. Dolan P, Bakhache W, Orr W, McCormick L. Uncovering Structural Plasticity of Enterovirus \nA through Deep Insertional and Deletional Scanning [Internet]. Research Square; 2024 [cited \n2025 Aug 12]. Available from: https://www.researchsquare.com/article/rs-3835307/v1  \n69. Macdonald CB, Nedrud D, Grimes PR, Trinidad D, Fraser JS, Coyote-Maestas W. DIMPLE: \ndeep insertion, deletion, and missense mutation libraries for exploring protein variation in \nevolution, disease, and biology. Genome Biol. 2023 Feb 24;24(1):36.  \n70. Coyote-Maestas W, Nedrud D, Okorafor S, He Y, Schmidt D. Targeted insertional \nmutagenesis libraries for deep domain insertion profiling. Nucleic Acids Res. 2020 Jan \n24;48(2):e11.  \n71. Nedrud D, Coyote-Maestas W, Schmidt D. A large-scale survey of pairwise epistasis \nreveals a mechanism for evolutionary expansion and specialization of PDZ domains. Proteins \n44 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint \n\nStruct Funct Bioinforma. 2021;89(8):899–914.  \n72. Potapov V, Ong JL, Kucera RB, Langhorst BW, Bilotti K, Pryor JM, et al. Comprehensive \nProfiling of Four Base Overhang Ligation Fidelity by T4 DNA Ligase and Application to DNA \nAssembly. ACS Synth Biol. 2018 Nov 16;7(11):2665–74.  \n73. Mateyko N, de Boer CG. Culture Wars: Empirically Determining the Best Approach for \nPlasmid Library Amplification. ACS Synth Biol. 2024 Aug 16;13(8):2328–34.  \n74. Haddox HK, Galloway JG, Dadonaite B, Bloom JD, Matsen FA, DeWitt WS. Jointly \nmodeling deep mutational scans identifies shifted mutational effects among SARS-CoV-2 spike \nhomologs [Internet]. bioRxiv; 2023 [cited 2023 Oct 4]. p. 2023.07.31.551037. Available from: \nhttps://www.biorxiv.org/content/10.1101/2023.07.31.551037v1  \n75. Otwinowski J, McCandlish DM, Plotkin JB. Inferring the shape of global epistasis. Proc Natl \nAcad Sci. 2018 Aug 7;115(32):E7550–8.  \n76. Yu TC, Thornton ZT, Hannon WW, DeWitt WS, Radford CE, Matsen FA IV, et al. A \nbiophysical model of viral escape from polyclonal antibodies. Virus Evol. 2022 Jul \n1;8(2):veac110.  \n77. Loes AN, Tarabi RAL, Huddleston J, Touyon L, Wong SS, Cheng SMS, et al. \nHigh-throughput sequencing-based neutralization assay reveals how repeated vaccinations \nimpact titers to recent human H1N1 influenza strains [Internet]. bioRxiv; 2024 [cited 2024 May \n17]. p. 2024.03.08.584176. Available from: \nhttps://www.biorxiv.org/content/10.1101/2024.03.08.584176v1  \n78. CDER Scientific Review. Emergency Use Authorization (EUA) for PEMGARDA Center for \nDrug Evaluation and Research (CDER) Review Memorandum [Internet]. U.S. Food & Drug \nAdministration; 2024. Available from: \nhttps://www.fda.gov/media/181308/download?attachment  \n79. UW Virology COVID-19 Dashboard [Internet]. [cited 2023 Nov 7]. Available from: \nhttps://depts.washington.edu/labmed/covid19/#sequencing-information  \n45 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted August 19, 2025. ; https://doi.org/10.1101/2025.08.18.671001doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}