Mutation-driven reorganisation of E2–RRV12 antibody binding interfaces across Ross River virus variants: A computational study

Mutation-driven reorganisation of E2–RRV12 antibody binding interfaces across Ross River virus variants: A computational study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mutation-driven reorganisation of E2–RRV12 antibody binding interfaces across Ross River virus variants: A computational study Md. Eram Hosen, Guendouzi Abdelkrim, Paul F. Horwood, Subir Sarker This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9535817/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Ross River virus (RRV) is the most prevalent arthritogenic alphavirus in Australia, yet the structural consequences of envelope protein variation on antibody recognition remain poorly understood. Here, we analysed 85 complete RRV E2 sequences from Australia associated with documented infections and compared them with the earliest available wild type isolate to identify recurrent amino acid substitutions. Five top mutant variants (M1–M5) were selected for detailed investigation. In silico predictions revealed that Y5H and R238K may destabilize local structure, while other substitutions variably modulate protein folding and surface exposure. Structural modelling and molecular docking with the broadly neutralizing RRV-12 Fab antibody indicated that M1 and M3 maintained interactions with epitopes similar to WT, whereas M2, M4, and M5 exhibited redistributed antibody contacts, with M4 showing the most pronounced shift, although overall binding energetics remained largely conserved. Molecular dynamics simulations revealed structural plasticity in surface-exposed loops, suggesting that these mutations may modulate antigenic presentation while preserving attachment and entry functions. These results reveal that naturally occurring E2 mutations can reshape antigenic surfaces without necessarily abolishing antibody recognition. Importantly, the conserved residue engaged by RRV-12 highlights a potential target for rational vaccine design aimed at eliciting broad, cross-protective immunity against circulating RRV variants. Ross River virus E2 glycoprotein spatiotemporal dynamics mutant variants antibody-antigen docking molecular dynamics simulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Ross River virus (RRV) is the most prevalent arthritogenic alphavirus in Australia and a leading cause of epidemic polyarthritis in the Asia–Pacific region [ 1 ]. Thousands of cases are reported annually in Australia, with outbreaks imposing significant healthcare and socioeconomic burdens [ 2 ]. Clinically, infection is characterized by fever, rash, myalgia, and often persistent polyarthritis that can last months to years, significantly impairing quality of life [ 1 , 3 ]. RRV is transmitted by mosquitoes, primarily Aedes and Culex species, and maintained in complex zoonotic transmission cycles involving marsupial reservoir hosts [ 4 ]. Despite decades of surveillance efforts, recurrent outbreaks underscore persistent gaps in our understanding of viral evolution, tissue tropism, and immune-mediated disease mechanisms. RRV is a positive-sense single-stranded RNA virus (~ 11.8–11.9 kb) within the family Togaviridae . The genome is organized into two major open reading frames separated by a conserved 26S subgenomic RNA promoter, encoding non-structural proteins (nsP1–nsP4) for replication and structural proteins (C, E3, E2, 6K/TF, and E1) that mediate virion assembly and entry (Fig. 1 a) [ 5 , 6 ]. Among these, the E2 glycoprotein is the principal surface-exposed envelope protein mediating host cell attachment and receptor engagement [ 7 ]. It interacts with cellular receptors such as MXRA8, along with additional attachment factors including heparan sulfate proteoglycans, thereby facilitating clathrin-mediated endocytosis and productive infection. The efficiency and specificity of receptor interactions influence cellular susceptibility, replication efficiency, and viral dissemination (Fig. 1 b) [ 7 – 9 ]. Beyond entry, this protein plays a central role in determining tissue tropism and inflammatory pathology. Receptor-dependent internalization permits infection of musculoskeletal and immune cell populations, including macrophages, synovial fibroblasts, and skeletal muscle cells. Infection of these cells triggers the release of pro-inflammatory mediators such as TNF-α, IL-6, and MCP-1, promoting immune cell recruitment and amplification of local inflammatory signalling. The resulting cytokine-driven cascade contributes to synovial inflammation, joint swelling, pain, and the chronic arthralgia characteristic of RRV disease (Fig. 1 c) [ 3 , 10 – 12 ]. Moreover, E2 is the principal target of neutralizing antibodies and therefore occupies a central position at the interface between viral fitness and host immunity. Following antigen presentation and CD4⁺ T cell–dependent B cell activation, E2-specific antibodies block receptor binding and promote Fc receptor–mediated viral clearance (Fig. 1 d) [ 7 , 13 , 14 ]. However, evolutionary pressure arising from host immune responses and vector–host transmission dynamics may favour amino-acid substitutions that enhance receptor affinity, optimize fusion efficiency, or diminish antibody recognition [ 15 ]. Such adaptive mutations may simultaneously modulate infectivity, tissue tropism, and immune escape, yet their mechanistic consequences remain incompletely defined. While environmental and vector-related factors are established drivers of RRV transmission [ 16 , 17 ], the role of viral genetic adaptation in shaping outbreak dynamics remains incompletely understood. Given that the E2 glycoprotein is the primary mediator of receptor engagement and antibody recognition, it is a likely target for adaptive selection. However, the lack of a comprehensive framework linking naturally occurring E2 substitutions to functional changes in viral entry and immune evasion limits our ability to interpret viral evolution or inform vaccine design. To address this gap, this study integrates spatiotemporal surveillance data with E2 sequence variation through phylogenetic, structural, and mutational analyses. Computational modelling, antibody docking, and molecular dynamics simulations were used to assess how substitutions may alter epitope accessibility and antibody interactions. Linking viral evolution to functional immune outcomes may provide mechanistic insight into RRV pathogenicity and help inform vaccine development against arthritogenic alphaviruses. Materials and Methods Spatiotemporal distribution of RRV in Australia Data on laboratory confirmed RRV cases in Australia from 1991 to 2025 were obtained from the National Notifiable Diseases Surveillance System (NNDSS) database ( https://nindss.health.gov.au/pbi-dashboard/ ) [ 18 ]. Only complete records including year of notification and state/territory of residence were included. Annual case counts were aggregated by year and by state/territory. Temporal trends were visualized using line graphs and bar charts. The geographic distribution of reported cases was mapped using administrative boundaries of Australian states and territories. Maps were created to illustrate regional differences in reported cases, while line and bar plots depicted temporal trends and relative incidence across states. Sequence retrieval, dataset curation and phylogenetic analysis Complete genome sequences of RRV were retrieved from the National Center for Biotechnology Information (NCBI) Virus database in October 2025. A total of 85 complete genomes originating from Australia were included. Sequences were filtered to exclude incomplete genomes, partial structural polyprotein regions, truncated non-structural proteins, or entries containing ambiguous nucleotide calls. Only high-quality, full-length coding sequences of the structural polyprotein (Capsid–E3–E2–6K–E1) were retained to ensure robust evolutionary and structural analyses. These sequences were aligned using the MUSCLE algorithm in MEGA X with default parameters [ 19 ]. The best-fit amino acid substitution model for phylogenetic analysis was determined using the model selection function in MEGA X based on the lowest Bayesian Information Criterion (BIC) score. Maximum-likelihood (ML) phylogenetic reconstruction was then performed using the selected substitution model, and branch support was assessed with 1,000 bootstraps replicates. The resulting phylogeny was used to characterize lineage clustering among Australian RRV isolates, providing a framework for downstream analyses of E2 variation. The E2 envelope glycoprotein was selected as the primary focus due to its critical roles in receptor engagement and antibody recognition. The E2 coding region (amino acids 348–750 of the canonical structural polyprotein) was extracted from each genome based on the annotation of the earliest available Australian isolate (1959 reference strain) and used for subsequent mutation, structural, and antibody-binding analyses. Multiple sequence alignment and mutation profiling The earliest RRV isolate in the dataset was designated as the reference E2 sequence (WT). Amino-acid substitutions were identified through multiple sequence alignment in MEGA X [ 19 ]. For each isolate, total non-synonymous substitutions relative to the reference were enumerated. Isolates were ranked according to mutation burden, and the five isolates harbouring the highest number of E2 substitutions were selected for downstream structural and computational analyses. Functional impact prediction of E2 substitutions All identified substitutions in the five selected isolates were evaluated for potential effects on protein stability and structural function using six complementary computational algorithms: I-Mutant 2.0 [ 20 ], MUpro [ 21 ], PolyPhen-2 [ 22 ], CUPSAT [ 23 ], DynaMut2 [ 24 ] and SIFT [ 25 ]. Default or algorithm-recommended parameters were applied. Mutations predicted by multiple tools to decrease structural stability or impair protein function were prioritized for structural interpretation and dynamic analysis. Wild structure and mutant variant modelling Three-dimensional models of the wild-type (WT) E2 glycoprotein and the five highly mutated variants were generated using SWISS-MODEL [ 26 ]. The closest available alphavirus E2 structural template with high sequence identity and structural resolution was selected for modelling. Wild type and mutant E2 glycoprotein docking interaction with antibodies As no experimentally resolved structure of an RRV-specific neutralizing antibody was previously available, we used Fab CHK-265 (also named as RRV-12 Fab), a broadly neutralizing human monoclonal antibody related to CHK-265 as a structural surrogate for our docking studies. Powell et al. (2020) identified RRV-12 as a Fab that binds a conserved epitope in the B domain of the RRV E2 glycoprotein, preventing attachment to the alphavirus receptor MXRA8 and potently neutralizing RRV infection in vitro and in vivo [ 27 ]. The cryo-electron microscopy structure of this antibody in complex with RRV (PDB ID: 6VYV) therefore provides a valid proxy to study antibody–E2 interactions and to assess the impact of naturally occurring E2 mutations on binding, despite the absence of a strictly RRV-specific Fab structure. Antibody–antigen docking between the Fab RRV-12 and each E2 structure (WT and variants) was performed using the HDOCK server [ 28 ]. Docked complexes were ranked by predicted binding energy scores. Interface residues, hydrogen-bond interactions, and epitope–paratope contacts were analysed using Discovery Studio Visualizer [ 29 ] to assess the structural consequences of E2 mutations on antibody engagement. Molecular dynamics simulations To assess the dynamic stability and conformational behaviour of the antibody–E2 complexes, molecular dynamics (MD) simulations were performed using YASARA Dynamics (v19.12.4) with the AMBER14 force field. Initially, the docked complexes were prepared by optimizing hydrogen bond networks and removing structural artifacts. The system was solvated using a TIP3P water model with a density of 0.997 g/mL, temperature set at 25°C, and pressure at 1 atm, employing steepest descent minimization to ensure proper hydration [ 30 , 31 ]. Physiological conditions were applied by neutralizing the system and adding 0.9% NaCl at pH 7.4 and a temperature of 310 K, approximating normal human body conditions (37°C) [ 32 , 33 ]. This ionic environment supports realistic protein stability and interactions with ligands [ 34 ]. A time step of 1.25 fs was used, and long-range electrostatics were calculated using the particle mesh Ewald (PME) method with an 8 Å cutoff [ 35 ]. Trajectories were saved every 100 ps over a total simulation of 100 ns. Post-simulation analyses included root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), solvent-accessible surface area (SASA), and radius of gyration (Rg) dynamics to evaluate complex stability and conformational changes. Comparative analyses between WT and mutated complexes were performed to quantify mutation-driven changes in structural stability and antibody-binding dynamics. Results and discussion Spatiotemporal dynamics of RRV infections in Australia Analysis of Australian RRV notification data (1991–2025) shows substantial inter-annual variability with episodic epidemic peaks. No cases were reported in 1991–1992, followed by a rise in the mid-1990s, peaking at 7,765 cases in 1996 and 6,612 in 1997. Notifications remained elevated in the early 2000s (~ 3,000–5,600 cases/year). A pronounced epidemic occurred between 2014–2017, with the highest peak in 2015 (9,532 cases), followed by another surge in 2017 (6,933 cases), likely reflecting favourable ecological and climatic conditions. A further increase occurred in 2020 (6,367 cases), with moderate endemic transmission persisting from 2021–2025 (1,711–3,375 cases/year) (Fig. 2 a, see supplementary file ). These trends highlight RRV’s cyclical, outbreak-prone dynamics and underscore the importance of continuous surveillance and early warning systems. State-level notifications reveal notable geographic heterogeneity. Queensland bore the highest cumulative burden (69,235 cases), showing repeated epidemic peaks in 1996, 2015, 2020, and 2024, reflecting tropical/subtropical vector-favorable conditions. New South Wales (26,018 cases) and Western Australia (21,589 cases) showed substantial but variable transmission, with notable surges during 1996–1997, 2015–2017, and 2020. Victoria (11,610 cases) had episodic spikes in 2011 and 2017, while Northern Territory (6,769 cases) and South Australia (6,844 cases) exhibited moderate, intermittent activity. Tasmania (754 cases) and the Australian Capital Territory (242 cases) consistently reported low incidence, constrained by climate and ecology (Fig. 2 b, see supplementary file ). These data are also illustrated in the accompanying map (Fig. 2 c), which visually highlights the uneven spatial distribution of RRV cases across Australia and showed that Queensland is the national hotspot for these viruses over multiple decades. Previous studies also reported northern and coastal regions as persistent hotspots with elevated incidence rates, while inland and southern areas exhibited lower and more variable transmission patterns [ 2 , 36 ]. National outbreaks during major epidemic years (1996–1997, 2015–2017, 2020) were interspersed with lower endemic transmission phases. Host distribution and geographic origin of complete RRV genome sequences A total of 85 complete RRV structural polyprotein sequences were analysed, representing isolates from multiple hosts and geographic regions across Australia. The majority originated from mosquito vectors (77%, n = 66), followed by human host (17%, n = 15), with smaller contributions from avian (4%, n = 3) and wallaby (2%, n = 2) sources (Fig. 2 d), reflecting the multi-host ecology of RRV transmission. Geographically, New South Wales accounted for the largest proportion of sequences (43%), followed by Queensland (24%) and the Northern Territory (12%), while Victoria, Tasmania, and other regions contributed fewer isolates (Fig. 2 e). Evolutionary divergence of the E2 glycoprotein and its correlation with epidemic waves Structural polyprotein based phylogenetic analysis of RRV strains in Australia includes the oldest available reference sample, AYI50356.1 (WT), an Ochlerotatus vigilax isolate from 1959 (see supplementary Fig. S1 ). This foundational lineage shows close temporal and genetic relationships with other early samples, including another 1959 O. vigilax isolate (ACV67002.1) and mid-1960s avian isolates such as Microeca fascinans and Poephila personata . Over several decades, the virus has demonstrated significant host plasticity, circulating among primary mosquito vectors including Culex annulirostris and various Ochlerotatus species and vertebrate reservoirs such as marsupials ( Notamacropus agilis ). Phylogenetic clustering further reveals repeated spillover into human populations ( Homo sapiens ), with documented infections spanning from 1971 to 2017. The overall tree topology, with a scale of 0.01, illustrates gradual genetic divergence from its 1959 origins into a diverse range of contemporary ecological niches across Australia. Notably, recent isolates from 2017–2018, including the human isolate AZF99254.1, cluster at the termini of extended lineages, indicating that while the virus has diverged significantly from its ancestral state, it remains ecologically stable and continues to utilize a wide array of mosquito vectors, including O. camptorhynchus and C. annulirostris . Temporal analysis of E2 glycoprotein sequences (1959–2018) revealed a progressive accumulation of naturally occurring substitutions, with early isolates (1960s–1980s) showing limited variation and post-1990 strains consistently harbouring core substitutions (Y5H, I54L, R238K, frequently D119N). Additional substitutions emerging from the mid-2000s onward (e.g., T371I, A356T, M363I, Y283H, A376T) were temporally coincident with several major epidemic periods, particularly the 2014–2017 outbreak ( Table S1 ). Although causality cannot be established, the temporal clustering of expanded E2 mutational profiles with high-incidence years is consistent with reports in other arboviruses showing that gradual sequence diversification can influence viral fitness and host adaptation over time, even under predominant purifying selection [ 37 ]. Similar genome-wide lineage turnover has been documented in RRV and linked to historical epidemic patterns [ 38 ]. However, climatic and ecological factors including rainfall, temperature and vector abundance remain the primary drivers of transmission variability across Australia [ 17 , 39 – 41 ]. These findings suggest that while environmental conditions largely determine outbreak magnitude, viral evolution within E2 may act as a contributory factor by subtly modulating antigenic properties and transmission dynamics over multi-year cycles. Structural architecture, compositional features and mutational landscape of the RRV E2 glycoprotein Two-dimensional structural annotation of the RRV E2 glycoprotein with a conserved length of 403 amino acids across all analysed strains, revealed the canonical alphavirus organization comprising a large extracellular domain enriched in α-helices and β-strands interconnected by flexible coil regions, followed by a single C-terminal transmembrane helix and a short cytoplasmic tail (Fig. 3 a). The extracellular region displayed structured β-sheet and helical cores interspersed with surface-exposed loops and short disordered segments, consistent with a requirement for conformational plasticity in receptor engagement and antigenic recognition [ 42 , 43 ]. The hydrophobic transmembrane segment at the C terminus appeared structurally conserved, supporting its essential role in membrane anchoring and virion assembly [ 44 , 45 ]. Amino acid composition analysis further demonstrated enrichment of threonine (9.20%), alanine (8.91%), proline (7.68%) and glycine (6.71%), whereas tryptophan (1.24%), methionine (1.49%) and asparagine (1.88%) were least represented (see supplementary file Table S2 ). This compositional bias may reflect broader structural constraints associated with protein stability and adaptability. However, as structural flexibility is inherently a local property, global amino acid composition alone does not permit direct inference regarding specific flexible or surface-exposed region. Comparative analysis of 85 isolates identified 335 naturally occurring amino acid substitutions within E2, corresponding to an average of ~ 3.9 substitutions per isolate and a per-site mutation frequency of 0.0098 (~ 0.98%) across the 403-residue glycoprotein (Fig. 3 c). Five isolates harbouring the highest number of substitutions were selected for detailed structural assessment ( Supplementary Table S2 ) and were designated M1 (formerly WAB51748.1; seven substitutions), M2 (WAB51716.1; seven substitutions), M3 (WAB51730.1; six substitutions), M4 (WAB51830.1; five substitutions) and M5 (WAB51818.1; five substitutions) (Fig. 3 d). Substitutions were distributed across both N-terminal and central regions of the protein. M1 contained Y5H, V19I, I54M, A105T, R238K, E289V and H291Q (Fig. 3 e); M2 carried Y5H, I54L, D119N, R238K, A356T, M363I and T371A (Fig. 3 f); M3 harboured Y5H, I54L, R238K, Y283H, E309V and A376T (Fig. 3 g); M4 included Y5H, M32K, I54L, R238K and L273F (Fig. 3 h); and M5 contained Y5H, I54L, K117M, R238K and M363V (Fig. 3 i). Notably, Y5H and R238K were conserved across all five variants, indicating potential functional relevance. Several additional substitutions are located in regions corresponding to predicted surface-exposed loop regions of the E2 protein based on structural modelling, which are commonly implicated in receptor interaction and antigenicity. Based on the identified substitutions, three-dimensional structures of the five mutant variants (M1–M5) (Fig. 3 e-i) and the wild-type E2 protein (Fig. 3 b) were generated using homology modelling with SWISS-MODEL to evaluate antigen-antibody interaction for potential alterations associated with the observed mutations. The predicted 3D structures revealed that recurrent substitutions, including Y5H, I54L/M and R238K, localize to structurally informative regions with potential influence on folding stability, surface accessibility and interaction interfaces with host receptors or neutralizing antibodies. These findings define the structural framework and mutational spectrum of the RRV E2 glycoprotein, providing a foundation for subsequent stability and dynamic analyses. Structural and functional impact of recurrent E2 protein mutations To predict the structural and functional impact of recurrent amino acid substitutions in the RRV E protein, we analysed the most frequently observed mutations using complementary in silico prediction tools, including I-Mutant, Mupro, PolyPhen-2, CUPSAT, and DynaMut2 ( Supplementary Table S3 ). Substitutions such as Y5H and R238K, conserved across the five most prevalent mutant isolates, were predominantly predicted to destabilize the protein. Specifically, Y5H consistently reduced stability (I-Mutant: − 0.98; Mupro: − 1.49) and was classified as “probably damaging” by CUPSAT and DynaMut2, suggesting potential alterations in folding and surface exposure. In contrast, R238K exhibited context-dependent effects, with I-Mutant and Mupro predicting decreased stability (–1.09, − 1.23), whereas DynaMut2 indicated stabilizing influences in certain structural environments (1.71), consistent with a role in modulating local conformational dynamics without compromising global envelope integrity. Other substitutions displayed a spectrum of predicted effects. For example, V19I and A105T were generally destabilizing, whereas I54M, D119N, M363V/I, and E289V were predicted to enhance stability according to I-Mutant and Mupro, although functional predictors (PolyPhen-2, CUPSAT) suggested these changes were tolerated or “possibly damaging.” Mutations localized to surface-exposed loops, such as L273F, Y283H, T371A, and A376T, were variably predicted to affect protein stability and function, potentially influencing receptor interactions or antigenicity. Mutational studies further underscore the biological relevance of E2 variation beyond sequence divergence. A single Y18H substitution in the E2 ectodomain markedly attenuates disease in a mouse model of alphavirus-induced musculoskeletal inflammation, demonstrating the capacity of specific mutations to alter viral fitness and pathogenesis [ 46 ]. Similarly, selection of RRV variants bearing substitutions in the E2 B domain (e.g., K189Q, D214A) under antibody pressure highlights the role of E2 mutations in shaping antigenic surfaces and neutralization sensitivity in vivo [ 47 ]. Functional analyses of E2 glycan mutants (N200Q, N262Q) reveal that removal of select glycans attenuates complement-mediated pathology, emphasizing the contribution of E2 structural features and post-translational modifications to immune-driven disease outcomes [ 48 ]. Overall, these observations indicate that recurrent substitutions in the RRV E protein impart diverse structural consequences, modulating local folding, surface accessibility, and potential host interactions while largely preserving global envelope architecture. This integrative perspective illuminates how RRV balances structural stability with antigenic flexibility to facilitate host adaptation and immune evasion. Comparative binding analysis of wild-type and mutant RRV E2 variants with the Fab RRV-12 neutralizing antibody Computational docking analysis revealed measurable differences in antibody–antigen interactions between the wild-type (WT) RRV E2 protein and selected mutant variants. The WT E2–Fab RRV-12 complex demonstrated the strongest predicted binding affinity, with a global binding energy of − 301.31 kcal/mol, serving as the reference for neutralization potential (Table 1 ). Among the mutant variants, M2 (–299.95 kcal/mol) and M3 (–299.06 kcal/mol) retained comparable binding energies to WT despite sequence variation (Table 1 ). In contrast, M1 (–295.53 kcal/mol) and M4 (–291.83 kcal/mol) showed a modest reduction in predicted affinity. Overall, the relatively narrow binding energy range (–301 to − 291 kcal/mol) suggests that Fab RRV-12 maintains broadly conserved binding potential across circulating variants. Table 1 Binding energies and interacting residues of wild-type and mutant RRV E2 proteins in complex with the Fab RRV-12 antibody. Binding free energies (kcal mol⁻¹) and interacting amino acid residues at the E2–Fab RRV-12 interface is presented for the wild-type (WT) and five mutant variants (M1–M5). All listed contact residues represent hydrogen bond interactions identified at the antigen–antibody interface. Here, E, I, and M are antigen E2 glycoprotein, RRV-12 antibody heavy chain, and light chain respectively. Complexes Binding energy Interacting amino acid Complexes Binding energy Interacting amino acid WT + RRV-12 -301.31 - Asp161 (E)-Gln1 (I) - Tyr16(E)-Gly26(I) - Arg165(E)- Thyr32 (I) - Tyr5(E)-Ala76(I) - Cys212(E)-Tyr100(I) - His213(E)- Tyr106(I) - Arg25(E)-Ser160(I) - Asn187(E)-Asn273(M) - Tyr186(E)-Arg274(M) - Gln234(E)-Ser389(M) M3 + RRV-12 -299.06 - Asp161(E)-Gln1(I) - Tyr16(E)-Gly26(I) - Arg165(E)-Tyr32(I) - His5(E)-Ala76(I) - Cys212(E)-Tyr100(I) - His213(E)-Tyr106(I) - Arg25(E)-Ser160(I) - Asn187(E)-Asn-273(M) - Tyr186(E)-Arg274(M) - Gln234(E)-Ser389(M) M1 + RRV-12 -295.53 - Asp161 (E)-Gln1 (I) - Tyr16(E)-Gly26(I) - His5(E)-Ala76(I) - Cys212(E)-Tyr100(I) - His213(E)- Tyr106(I) - Glu27(E)-Ser160(I) - Arg237(E)-Ser165(I) - Glu153(E)-Ser165(I) - Gln234(E)-Pro260(M) - Tyr186(E)-Arg274(M) - Asn194(E)-Arg274(M) M4 + RRV-12 -291.83 - Tyr346(E)-Ile2(I) - Tyr346(E)-Leu4(I) - Glu340(E)-Arg9(I) - Glu331(E)-Gln109(I) - Glu268(E)-Thr78(I) - His335(E)-Leu156(I) - His335(E)-Thr169(I) - His339(E)-Lys387(M) M2 + RRV-12 -299.95 - Leu365(E)-Arg9(I) - Arg380(E)-Asn88(I) - Arg380(E)-Thr114(I) - Cys372(E)-Ala172(I) - Cys403(E)-Gln175(I) - Glu340(E)-Asn190(I) - His339(E)-Asn190(I) - Leu366(E)-His262(M) M5 + RRV-12 -298.70 - Arg380(E)-Asn88(I) - Arg380(E)-Thr114(I) - Cys372(E)-Ala172(I) - Cys403(E)-Gln175(I) - Glu340(E)-Asn190(I) - His339(E)-Asn190(I) - Leu366(E)-His262(M) Detailed interface comparison revealed that the WT E2–RRV-12 complex is characterized by hydrogen bond interactions primarily within the N-terminal and central regions of E2 involving residues Tyr5, Tyr16, Arg25, Asp161, Arg165, Asn187, Tyr186, Cys212, His213 and Gln234 of E2. These residues interact with Fab RRV-12 antibody residues Gln1, Gly26, Tyr32, Ala76, Tyr100, Tyr106, Ser160, Asn273, Arg274 and Ser389, which defining a structurally coordinated conformational epitope recognized by the RRV-12 antibody (Fig. 4 a). In M3, the interaction pattern remained largely conserved relative to WT, with His5 replacing Tyr5 while maintaining overall same binding footprint, indicating preservation of the native epitope architecture (Fig. 4 d). In M1 exhibited partial reorganization of the interface, with additional contacts involving Glu27, Arg237, Glu153 and Asn194, suggesting local reshaping of the antibody-binding surface while retaining several core residues such as His5, Cys212, His213 and Tyr186 (Fig. 4 b). In contrast, M2 (Fig. 4 c) and M5 (Fig. 4 f), displayed altered docking poses characterised by a shift in dominant contact regions toward the C-terminal region of E2, involving residues Arg380, Cys372, Cys403, Glu340, His339 and Leu366. Notably, these variants also exhibited differences in the overall orientation of the antibody relative to the antigen compared to the WT complex, suggesting that the observed changes may reflect alternative docking conformations rather than simple local shifts in contact residues. The M4 variant displayed the most distinct binding profile, with interactions concentrated around residues Tyr346, Glu331, Glu268, His335 and His339 (Fig. 4 e). However, the predicted interaction interface in this variant differs from the WT binding configuration and does not fully recapitulate the characteristic interaction pattern observed in the reference complex, indicating a potentially lower-confidence or alternative docking pose. Overall, integration of binding energy and interface analyses suggests that WT and M3 maintain a conserved binding interface, M1 shows partial interface reorganisation and M2, M4 and M5 displayed altered binding. However, given the inherent limitations of computational docking, the possibility of pose-dependent artifacts cannot be excluded, and the results should be interpreted as predictive rather than definitive. These findings indicate that naturally occurring substitutions can remodel the spatial or altered configuration of the antibody-binding surface without abolishing interaction capacity, consistent with emerging structure-function relationships in alphaviruses where sequence variation reshapes immunogenic topography. Broadly neutralizing monoclonal antibodies across arthritogenic alphaviruses predominantly target structurally conserved regions of the E2 glycoprotein, particularly within the B domain, and substitution of key residues within these epitopes markedly alters antibody binding profiles and neutralization potency [ 49 ]. The preservation of crucial interfacial residues and comparable binding energetics between wild-type and the M3 variant suggests maintenance of antigenic stability, supporting the classification of RRV-12 as a broadly neutralizing antibody capable of recognizing diverse E2 variants, as described for human mAbs that engage conserved footprints on RRV, Chikungunya and Mayaro virus E2 [ 27 ]. In contrast, the epitope redistribution observed in M1, M2, M4 and M5 especially the altered interaction profile in M4 demonstrates that substitutions can shift or altered antibody contact sites and subtly reshape binding architecture (Fig. 4 ). This pattern is consistent with alphavirus escape variants identified under antibody pressure, where mutations in E2 domain B residues reduce neutralization sensitivity without abolishing overall structural integrity [ 49 ]. Although interactions remain energetically favourable, spatial reorganization of surface-exposed regions may alter antibody accessibility, orientation, and neutralization efficiency, facilitate immune escape and viral evolution while preserve key determinants of attachment and entry. From a translational perspective, the relative conservation of binding profiles suggests that vaccines based on wild-type E2 antigens could provide cross-protective immunity against circulating variants; nevertheless, continued surveillance of epitope-altering substitutions is essential, as their accumulation in major antigenic sites can progressively diminish recognition and promote immune evasion in alphaviruses [ 50 ]. Analysis of dynamic stability and flexibility of antigen–antibody complexes RMSD analysis and structural stability of antigen–antibody complexes The average backbone RMSD values for all antigen–antibody complexes ranged from 0.34 to 1.67 Å [WT (0.34), M2 (1.06), M3 (1.48), M1 (1.16), M5 (1.67), and M4 (1.01)], indicating preserved global structural stability with only moderate conformational adaptation in selected mutants. All trajectories showed an initial RMSD increase during equilibration (0–20 ns) (Fig. 5 a). The initial RMSD increase reflects relaxation from the docked conformation and solvent accommodation during equilibration, a common feature of antibody–antigen MD simulations rather than structural destabilization [ 51 , 52 ]. During the post- equilibration phase (20–100 ns), RMSD profiles stabilized with minimal fluctuations and no sustained upward drift, abrupt spikes, or signs of dissociation (Fig. 5 a). The maintenance of RMSD values below 2 Å across all systems suggests that global antigen–antibody architecture remained stable during the simulation. The WT complex exhibited the lowest RMSD, serving as a stable structural benchmark. Mutants displayed modestly elevated RMSD values, consistent with localized conformational adjustments within the epitope–paratope interface rather than large-scale structural perturbations. Such adaptive micro-rearrangements are characteristic of alphavirus E2 glycoprotein flexibility, particularly within surface-exposed B-domain regions known to accommodate antibody binding while maintaining functional constraints required for receptor engagement and viral entry [ 49 , 53 , 54 ]. Structural analyses of arthritogenic alphaviruses have similarly demonstrated that neutralizing antibodies can tolerate limited epitope plasticity without loss of overall complex integrity, reflecting a balance between immune pressure and envelope protein stability [ 27 , 53 , 55 , 56 ]. These findings indicate that E2 substitutions enhance interface-level adaptability without destabilizing the antigen–antibody complex. This structural plasticity permits subtle shifts in binding orientation and contact networks that may modulate neutralization efficiency while preserving antibody engagement and envelope integrity. Such E2 mutations remodel antigenic surfaces without compromising assembly or entry functions. So, this MD data suggest that recurrent E2 mutations fine-tune interaction dynamics rather than disrupt global architecture, supporting a mechanism of partial immune adaptation within structurally constrained viral envelopes. Radius of gyration (Rg) analysis and global compactness of antigen–antibody complexes The average backbone Rg values demonstrated that all antigen–antibody complexes maintained overall structural compactness throughout the 100 ns simulations (Fig. 5 b). Mean Rg values were WT (3.4661 Å), 23 (5.0758 Å), M3 (3.7392 Å), M1 (3.8496 Å), M5 (4.3484 Å), and M4 (1.7574 Å). Despite quantitative differences among variants, Rg values remained within relatively narrow and stable ranges over time, with no sudden expansions or abrupt fluctuations indicative of structural collapse or unfolding (Fig. 5 b). These findings suggest that both WT and mutant antigen–antibody complexes preserved stable global folding, indicating that the investigated mutations do not induce large-scale structural destabilization of the RRV E protein during antibody engagement. Trend analysis revealed that from the start of the simulation to approximately 50 ns, all complexes except M5 displayed stable Rg profiles, followed by a slight decreasing trend suggesting gradual structural relaxation and compaction after initial equilibration (Fig. 5 b). Complexes M2 and M4 maintained overall stability with temporal patterns comparable to WT and other mutants; however, their consistently higher Rg values indicate comparatively less compact structural assemblies throughout the simulation [ 51 , 57 ]. This may reflect altered epitope orientation or expanded interfacial arrangements resulting from alternative binding pockets identified during docking analysis (Fig. 4 c & e ). In contrast, M1 and M3 exhibited Rg values closer to the WT profile, indicating WT-like compactness and structural organization. A distinct Rg trajectory was observed for M5. This complex initially followed a stable trend similar to other systems up to ~ 10 ns, followed by a transient increase between 10–20 ns, likely reflecting early conformational rearrangement associated with alternative binding pocket accommodation (Fig. 5 b). Subsequently, the Rg gradually decreased until ~ 60 ns and stabilised thereafter, suggesting successful structural adaptation and attainment of a compact, energetically favourable conformation during later simulation stages (Fig. 5 b). The consistently stable Rg values across all complexes indicate that mutations and binding pocket shifts do not compromise the overall compactness or structural integrity of the antigen–antibody assemblies. The absence of significant Rg expansion supports the conclusion that mutations do not promote unfolding or loosening of the E protein structure upon antibody binding [ 51 , 57 , 58 ]. Comparable stability profiles between WT-like complexes and mutants engaging alternative epitopes suggest that distinct binding modes converge toward similarly stable structural architectures [ 49 , 53 , 59 ]. This reflects evolutionary constraints on the RRV E protein, where surface-level adaptations allow modified antigenic interactions while preserving the conserved global fold essential for viral fitness and immune recognition [ 7 ]. Consequently, differential epitope usage appears to modulate local conformational landscapes without disrupting the structural robustness of the immune complex. Solvent-accessible surface area (SASA) analysis and antigen exposure dynamics SASA profiles of all antigens–antibody complexes remained largely stable throughout the 100 ns simulation period, displaying only minor fluctuations that are consistent with normal dynamic breathing motions of protein complexes. Mean SASA values were WT (461.7465 Å 2 ), M2 (463.0063 Å 2 ), M3 (461.6494 Å 2 ), M1 (450.1827 Å 2 ), M5 (474.1834 Å 2 ), and M4 (469.0182 Å 2 ). The relatively narrow SASA range (~ 450–474) across all systems indicates consistent solvent exposure and preservation of antigen surface accessibility despite the presence of mutations and altered binding modes (Fig. 5 c). Trend analysis revealed that most complexes maintained stable SASA trajectories throughout the simulation, supporting the absence of major conformational rearrangements affecting overall solvent exposure. Notably, M1 exhibited a distinct dynamic pattern after ~ 50 ns, with a gradual decrease in SASA until ~ 75 ns, followed by a transient increase up to ~ 90 ns, and a final decline toward the end of the simulation (Fig. 5 c). This behaviour likely reflects local conformational adjustments at the antigen–antibody interface rather than global structural changes, suggesting transient tightening and relaxation of surface regions during complex stabilisation. These stable SASA values across both WT-like and alternative-pocket-binding mutants indicate that mutations do not induce significant epitope burial or large-scale shielding of antigenic surfaces during antibody engagement. Instead, the maintenance of comparable solvent exposure supports the concept that epitope switching arises from subtle surface reconfiguration rather than immune evasion through structural masking. This pattern contrasts with highly immune-evasive viruses that rely on extensive antigenic shielding. The preserved SASA profiles therefore suggest that RRV evolution favours partial immune modulation, where mutations enable adaptive redirection of antibody interactions while maintaining accessible antigenic surfaces and conserving overall structural integrity essential for viral function. Root means square fluctuation (RMSF) analysis Root means square fluctuation (RMSF) analysis over the 100 ns simulations revealed distinct yet coordinated flexibility patterns across the RRV E antigen (Chain-E) and the antibody heavy (Chain-I) and light (Chain-M) chains, highlighting functionally relevant structural dynamics within the antigen–antibody complexes. The WT complex exhibited the lowest average RMSF (0.6091), whereas M1 (2.0061), M4 (1.7574), and M3 (1.697) showed comparatively higher flexibility, with M5 (1.3820) and M2 (1.1209) displaying moderate fluctuations. Within the 403-residue RRV E protein, moderate localized peaks (2–2.5 Å) were observed in regions 47–49, 146–153, 167–184, and 339–360 (Fig. 5 d), likely corresponding to surface-exposed loops and epitope regions that retain intrinsic flexibility to facilitate antibody accommodation and adaptive binding [ 60 , 61 ]. Pronounced fluctuation in the C-terminal segment (374–403) was detected in most complexes except M5 and M2, suggesting that these antibodies confer enhanced stabilization of the membrane-proximal region (Fig. 5 d). The antibody heavy chain exhibited moderate but controlled mobility, reflecting paratope adjustments required for stable antigen recognition without compromising structural integrity; notable fluctuations were primarily observed in M1. In contrast, the light chain displayed comparatively higher fluctuations in selected complexes (M3, M1, and M4) (Fig. 5 d), indicating supportive conformational plasticity that may facilitate interface optimization and binding adaptability. Importantly, the absence of widespread high RMSF values across any chain indicates that the mutations do not induce global destabilization but instead promote localized flexibility within functional regions [ 62 , 63 ]. These findings support a model in which RRV E protein mutations enable subtle epitope rearrangements and dynamic refinement of the binding interface while preserving overall immune complex stability, thereby allowing adaptive antigen–antibody interactions without disrupting structural architecture or functional recognition. Limitation of the study While this study integrates epidemiological, sequence, and structural analyses to investigate RRV E2 mutations, it is limited by the reliance on 85 sequences, the focus on a single monoclonal antibody (RRV-12), and the use of in silico predictions without experimental validation. Molecular dynamics simulations were performed on isolated antigen–antibody complexes and may not fully capture the influence of full virion architecture, glycosylation, or host factors. Consequently, the effects of additional variants, alternative antibodies, and in vivo interactions remain to be explored in future studies. Conclusion This integrated analysis combines epidemiological, sequence, and structural insights to reveal how recurrent substitutions in RRV E2 precisely remodel epitope landscapes and modulate antibody engagement. Mutant variants M1 and M3 maintained binding to epitopes nearly identical to WT, whereas M2, M4, and M5 exhibited redistributed antibody contacts, with M4 showing the most pronounced shift, yet overall binding stability was preserved. Such structural plasticity may support viral immune evasion and adaptive evolution while maintaining critical determinants for host-cell attachment. Critically, conserved antibody-binding regions revealed through RRV-12 interactions represent high-value targets for rational vaccine design. Exploiting these structurally constrained epitopes offers the potential to generate broadly cross-protective immunity against circulating RRV variants. These findings provide a mechanistic framework linking sequence variation, antigenic flexibility, and neutralization dynamics, establishing a foundation for next-generation, structure-guided alphavirus vaccines that anticipate viral evolution while maintaining protective efficacy. Declarations Competing interests The authors declare no competing interests. Funding Declaration The authors declare that no funding was received for this work. Author Contribution Md. Eram Hosen: Conceptualization, methodology, investigation, data curation, formal analysis, visualization, and original draft preparation. Guendouzi Abdelkrim: Molecular dynamics simulations. Paul F. Horwood: Supervision, manuscript review and editing, and critical revision of the intellectual content. Subir Sarker: Conceptualization, Supervision, project administration, and overall guidance of the study. Acknowledgement Subir Sarker is the recipient of an Australian Research Council Discovery Early Career Researcher Award (grant number DE200100367) funded by the Australian Government. The Australian Government had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data availability The data that supports the findings of this study are available in the supplementary material of this article and available to the corresponding upon reasonable request. References Zaid A, Burt FJ, Liu X, Poo YS, Zandi K, Suhrbier A, Weaver SC, Texeira MM, Mahalingam S (2021) Lancet Infect Dis 21(5):e123 Hosen ME, Dunsdon S, Sarker S (2026) Virology 617:110825 Liu X, Tharmarajah K, Taylor A (2017) Microbes Infect 19(11):496 Hu W, Mengersen K, Dale P, Tong S (2010) Environ Entomol 39(1):88 Abdullah N, Ahemad N, Aliazis K, Khairat JE, Lee TC, Abdul Ahmad SA, Adnan NAA, Macha NO, Hassan SS (2021) Viruses 13(6):1021 Luo D, Tan YB, Law MCY, Jin J (2025) Annual Review of Virology 12 Powell LA, Miller A, Fox JM, Kose N, Klose T, Kim AS, Bombardi R, Tennekoon RN, de Silva AD, Carnahan RH (2020) Cell Host Microbe 28(5):699 Kesari AS, Sharkey CM, Sanders DA (2019) Virology 529:177 Zhang R, Kim AS, Fox JM, Nair S, Basore K, Klimstra WB, Rimkunas R, Fong RH, Lin H, Poddar S, Crowe JE Jr., Doranz BJ, Fremont DH, Diamond MS (2018) Nature 557(7706):570 Morrison TE, Whitmore AC, Shabman RS, Lidbury BA, Mahalingam S, Heise MT (2006) J Virol 80(2):737 Mostafavi H, Tharmarajah K, Vider J, West NP, Freitas JR, Cameron B, Foster PS, Hueston LP, Lloyd AR, Mahalingam S, Zaid A (2022) PLoS Pathog 18(2):e1010185 Tappe D, Perez-Giron JV, Gomez-Medina S, Günther S, Munoz-Fontela C, Schmidt-Chanasit J (2017) Emerg Infect Dis 23(4):702 Davies JM, Cai YP, Weir RC, Rowley MJ (2000) Virology 275(1):67 Kerr PJ, Fitzgerald S, Tregear GW, Dalgarno L, Weir RC (1992) Virology 187(1):338 Hosen ME, Supti SJ, Horwood PF, Sarker S (2026) Rev Med Virol 36(1):e70082 Bi P, Hiller JE, Cameron AS, Zhang Y, Givney R (2009) Epidemiol Infect 137(10):1486 Tong S, Hu W, Nicholls N, Mackenzie J, Dale P, Wolff R et al (2007) Epidemiology 18(5):S103 (CDC) ACfDC (2025) National Notifiable Disease Surveillance System. Australian Government Kumar S, Stecher G, Li M, Knyaz C, Tamura K (2018) Mol Biol Evol 35(6):1547 Capriotti E, Fariselli P, Casadio R (2005) Nucleic Acids Res 33(suppl2):W306 Cheng J, Randall A, Baldi P (2006) Proteins: Structure. Function Bioinf 62(4):1125 Adzhubei I, Jordan DM, Sunyaev SR (2013) Current protocols in human genetics 76(1):7.20. 1 Parthiban V, Gromiha MM, Schomburg D (2006) Nucleic acids research 34(suppl_2):W239 Rodrigues CH, Pires DE, Ascher DB (2021) Protein Sci 30(1):60 Kumar P, Henikoff S, Ng PC (2009) Nat Protoc 4(7):1073 Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, Heer FT, de Beer TAP, Rempfer C, Bordoli L (2018) Nucleic acids research 46(W1):W296 Powell LA, Miller A, Fox JM, Kose N, Klose T, Kim AS, Bombardi R, Tennekoon RN, Dharshan, de Silva A, Carnahan RH, Diamond MS, Rossmann MG, Kuhn RJ, Crowe JE Jr (2020) Cell Host & Microbe 28(5):699 Yan Y, Tao H, He J, Huang S-Y (2020) Nat Protoc 15(5):1829 Visualizer D (2005) Accelrys software inc 649 Harrach MF, Drossel B (2014) J Chem Phys 140(17) Hosen ME, Jahan Supti S, Akash S, Rahman ME, Faruqe MO, Manirujjaman M, Acharjee UK, Gaafar A-RZ, Ouahmane L, Sitotaw B, Bourhia M, Zaman R (2023) Frontiers in Chemistry Volume 11–2023 Krieger E, Dunbrack RL Jr, Hooft RW, Krieger B (2011) Assignment of protonation states in proteins and ligands: combining pKa prediction with hydrogen bonding network optimization. Springer, Computational drug discovery and design, p 405 Roqunuzzaman M, Islam A, Supti SJ, Rifat MH, Islam MS, Ananna UH, Tusher KS, Al-Mutairi AA, Zaki MEA, Sarker S, Hosen ME (2025) J Comput Aided Mol Des 39(1):81 Supti SJ, Hosen ME, Karikor MAS, Hasan MZ, Chowdhury MM, Hossain MM, Priota MF, Nuryay MN, Hasan BM, Islam MM (2025) ChemistrySelect 10(18):e202405525 Essmann U, Perera L, Berkowitz ML, Darden T, Lee H, Pedersen LG (1995) J Chem Phys 103(19):8577 Qian W, Hurst C, Glass K, Harley D, Viennet E (2021) Trop Med Infect Disease 6(3):145 Pyke AT, Wilson DJ, Michie A, Mackenzie JS, Imrie A, Cameron J, Doggett SL, Haniotis J, Herrero LJ, Caly L, Lynch SE, Mee PT, Madzokere ET, Ramirez AL, Paramitha D, Hobson-Peters J, Smith DW, Weir R, Sullivan M, Druce J, Melville L, Robson J, Gibb R, van den Hurk AF, Duchene S (2024) Virus Evol 10(1) Michie A, Dhanasekaran V, Lindsay MDA, Neville PJ, Nicholson J, Jardine A, Mackenzie JS, Smith DW, Imrie A (2020) J Virol 94(2) Damtew YT, Varghese BM, Anikeeva O, Tong M, Hansen A, Dear K, Zhang Y, Morgan G, Driscoll T, Capon T (2024) The Lancet Regional Health–Western Pacific 48 Hime NJ, Wickens M, Doggett SL, Rahman K, Toi C, Webb C, Vyas A, Lachireddy K (2022) Australian and New Zealand. J Public Health 46(6):842 Koolhof IS, Beeton N, Bettiol S, Charleston M, Firestone SM, Gibney K, Neville P, Jardine A, Markey P, Kurucz N (2024) PLoS Pathog 20(2):e1011944 Li L, Jose J, Xiang Y, Kuhn RJ, Rossmann MG (2010) Nature 468(7324):705 Zimmerman O, Holmes AC, Kafai NM, Adams LJ, Diamond MS (2023) J Clin Investig 133(2) Byrd EA, Kielian M (2017) mBio 8(6). 10.1128/mbio.01564 Schmaljohn AL, McClain D (1996) Medical Microbiology 4th edition Jupille HJ, Medina-Rivera M, Hawman DW, Oko L, Morrison TE (2013) J Virol 87(10):5970 Fox JM, Huang L, Tahan S, Powell LA, Crowe JE Jr., Wang D, Diamond MS (2020) PLoS Pathog 16(8):e1008743 Gunn BM, Jones JE, Shabman RS, Whitmore AC, Sarkar S, Blevins LK, Morrison TE, Heise MT (2018) Virology 515:250 Fox JM, Long F, Edeling MA, Lin H, van Duijl-Richter MKS, Fong RH, Kahle KM, Smit JM, Jin J, Simmons G, Doranz BJ, Crowe JE Jr., Fremont DH, Rossmann MG, Diamond MS (2015) Cell 163(5):1095 Torres-Ruesta A, Chee RS, Ng LFP (2021) Microorganisms 9(5) Karplus M, McCammon JA (2002) Nat Struct Biol 9(9):646 Lemkul JA (2018) Living J Comput Mol Sci 1(1):5068 Long F, Fong RH, Austin SK, Chen Z, Klose T, Fokine A, Liu Y, Porta J, Sapparapu G, Akahata W, Doranz BJ, Crowe JE Jr., Diamond MS, Rossmann MG (2015) Proc Natl Acad Sci U S A 112(45):13898 Snyder AJ, Sokoloski KJ, Mukhopadhyay S (2012) J Virol 86(6):3100 Porta J, Jose J, Roehrig JT, Blair CD, Kuhn RJ, Rossmann MG (2014) J Virol 88(17):9616 Voss JE, Vaney MC, Duquerroy S, Vonrhein C, Girard-Blanc C, Crublet E, Thompson A, Bricogne G, Rey FA (2010) Nature 468(7324):709 Lobanov MY, Bogatyreva N, Galzitskaya O (2008) Mol Biol 42(4):623 Arnittali M, Rissanou AN, Harmandaris V (2019) Procedia Comput Sci 156:69 Murin CD, Wilson IA, Ward AB (2019) Nat Microbiol 4(5):734 Kocer I, Celik E (2024) J Biomol Struct Dynamics 42(9):4699 Maruyama Y, Igarashi R, Ushiku Y, Mitsutake A (2023) J Chem Inf Model 63(5):1529 Liu Cn, Denzler LM, Hood OE, Martin AC (2024) Do antibody CDR loops change conformation upon binding? Taylor & Francis, MAbs, p 2322533 Verkhivker GM, Agajanian S, Kassab R, Krishnan K (2022) J Chem Inf Model 62(8):1956 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 17 May, 2026 Reviews received at journal 10 May, 2026 Reviewers agreed at journal 10 May, 2026 Reviewers invited by journal 10 May, 2026 Editor assigned by journal 07 May, 2026 Submission checks completed at journal 28 Apr, 2026 First submitted to journal 26 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9535817","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":629958910,"identity":"6a22cb8c-4608-4ea5-9816-a6aa35f78a57","order_by":0,"name":"Md. Eram Hosen","email":"","orcid":"","institution":"James Cook University","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Eram","lastName":"Hosen","suffix":""},{"id":629958911,"identity":"b3b90340-b3f7-4f04-b94b-23fdbff36d11","order_by":1,"name":"Guendouzi Abdelkrim","email":"","orcid":"","institution":"University of Saida - Dr Moulay Tahar","correspondingAuthor":false,"prefix":"","firstName":"Guendouzi","middleName":"","lastName":"Abdelkrim","suffix":""},{"id":629958912,"identity":"3f6c3da3-131f-4dac-bc82-610d40c07be1","order_by":2,"name":"Paul F. Horwood","email":"","orcid":"","institution":"James Cook University","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"F.","lastName":"Horwood","suffix":""},{"id":629958913,"identity":"513a7275-c44b-4e65-9b55-11046b931700","order_by":3,"name":"Subir Sarker","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYFACHoYDUJqB4QMPmGlAvBbGGcRqgdPMUDZ+LfIzcg8ermCokzfvOXzss42MXWIDe/M2CYaawzi1GNzISzh4huGw4Zyzbcmzc3iSExt4jpVJMBzDo0Uix+BgA8MBxhn8PMbMOTwHEhskcswkGNhwa5GfAdZSZz+Dn/8zswVIi/wboJZ/uLUw3ABrYU6cwdvDzMwAtoXHTIKxDY/DzrxLONhgcDh5Bs8xY8YenmTjNp60YovEvnTcDmvPPfyxoaLOdgZP8mOGnz12sv3shzfe+PDNGrfDIHZBacYeBgY2ECOBgAYk8IN4paNgFIyCUTByAACD7E48godrOwAAAABJRU5ErkJggg==","orcid":"","institution":"James Cook University","correspondingAuthor":true,"prefix":"","firstName":"Subir","middleName":"","lastName":"Sarker","suffix":""}],"badges":[],"createdAt":"2026-04-27 03:26:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9535817/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9535817/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108013778,"identity":"58992b0f-2073-404a-9340-c26cd0872173","added_by":"auto","created_at":"2026-04-28 13:24:28","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":335396,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenomic features and multifunctional role of RRV E2 glycoprotein.\u003c/strong\u003e \u003cstrong\u003e(a)\u003c/strong\u003e Genomic organization of RRV (not in scale). \u003cstrong\u003e(b)\u003c/strong\u003e E2 mediates viral entry by binding cellular receptors such as MXRA8 and attachment factors, enabling internalization and cytoplasmic replication. \u003cstrong\u003e(c) \u003c/strong\u003eE2-driven infection of macrophages, synovial fibroblasts, and skeletal muscle cells induces pro-inflammatory cytokines (TNF-α, IL-6, MCP-1), promoting immune cell recruitment, local inflammation, and joint pathology. \u003cstrong\u003e(d)\u003c/strong\u003e E2 is the primary target of neutralizing antibodies; CD4⁺ T cell–dependent B cell responses block viral attachment and mediate clearance. Sequence variation in E2 can modify epitopes and allow immune escape. Created with BioRender (https://www.biorender.com/).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9535817/v1/43962ebf5ddb56ccb27d45bd.jpeg"},{"id":108014170,"identity":"461b2066-9973-498b-ac6c-56364e8ad587","added_by":"auto","created_at":"2026-04-28 13:25:48","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":339762,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatiotemporal dynamics of RRV infections and distribution of sequenced isolates in Australia. a) \u003c/strong\u003eAnnual notified RRV cases in Australia, illustrating cyclical epidemic waves and inter-epidemic periods.\u003cstrong\u003e b) \u003c/strong\u003eState-wise distribution of notified RRV cases over the same period, demonstrating regional heterogeneity in transmission intensity.\u003cstrong\u003e c) \u003c/strong\u003eGeographic map depicting national hotspots of RRV infection based on cumulative case notifications\u003cstrong\u003e. \u003c/strong\u003eEpidemiological data in panels a–c were obtained from the publicly accessible National Notifiable Diseases Surveillance System (NNDSS) from 1991 to 2025.\u003cstrong\u003e d) \u003c/strong\u003eHost distribution of complete RRV genome sequences collected between 1959 and 2018, demonstrating representation across mosquito vectors and vertebrate hosts.\u003cstrong\u003ee) \u003c/strong\u003eGeographic origin of 86 complete RRV genome sequences retrieved from the NCBI Virus database, showing spatial distribution of sequenced isolates across Australian states and territories (\u003cstrong\u003esee supplementary file\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9535817/v1/a0d49447a0b5faf86fc113f2.jpeg"},{"id":108013671,"identity":"9f68501e-d0c7-47c6-bf50-173cc90be815","added_by":"auto","created_at":"2026-04-28 13:24:01","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":604641,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural architecture, compositional features and mutational landscape of the RRV E2 glycoprotein. a) \u003c/strong\u003eTwo-dimensional schematic representation of the RRV E2 glycoprotein (403 amino acids). \u003cstrong\u003eb)\u003c/strong\u003eThree-dimensional predicted structure of the wild-type (WT) RRV E2 protein. \u003cstrong\u003ec) \u003c/strong\u003eSummary of amino acid substitutions identified across 85 RRV E2 isolates compared with the WT reference sequence. \u003cstrong\u003ed)\u003c/strong\u003eDistribution and positional mapping of mutations in five selected representative E2 variants (M1–M5). The number of amino acid changes per isolate and their respective residue positions along the 403-aa sequence are shown, demonstrating isolate-specific mutational patterns. \u003cstrong\u003e(e–i)\u003c/strong\u003eThree-dimensional predicted structures of mutant E2 proteins (M1–M5), modelled based on their respective amino acid substitutions. Mutated residues are highlighted and mapped onto the protein surface to illustrate their spatial localization within protein surface.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9535817/v1/bb23107af6492a20d00b0104.jpeg"},{"id":108013696,"identity":"f5a1addc-72f3-4e7d-bdbf-de16a0d77116","added_by":"auto","created_at":"2026-04-28 13:24:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1558610,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular docking analysis of RRV E2 wild type and mutant variants in complex with RRV-12 Fab antibody. a) \u003c/strong\u003eSurface representation and binding pose of the WT E2 glycoprotein in complex with the RRV-12 Fab antibody, highlighting interactions with heavy- and light-chain residues at the antigen–antibody interface. (\u003cstrong\u003eb–f) \u003c/strong\u003eDocking complexes of E2 mutant variants M1–M5 with RRV-12 Fab. Variants M1 and M3 engage an epitope largely overlapping with that of WT, maintaining a similar binding orientation and contact footprint. In contrast, substitutions in M2, M4, and M5 redistribute antibody contact residues and altered the binding interface, resulting in altered epitope positioning relative to WT. These models illustrate mutation-driven remodelling of antigenic surfaces while preserving overall interaction capacity.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9535817/v1/f8d92a2b02405cdbe1e26459.png"},{"id":108013806,"identity":"73c93518-2f80-4411-a8d0-19511a323a85","added_by":"auto","created_at":"2026-04-28 13:24:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":339883,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular dynamics analysis of RRV E2–RRV-12 Fab complexes over 100 ns.\u003c/strong\u003e(\u003cstrong\u003ea–d)\u003c/strong\u003e Structural stability and dynamics of WT and mutant variants (M1–M5) in complex with RRV-12 Fab antibody. \u003cstrong\u003ea)\u003c/strong\u003e Backbone RMSD trajectories indicate overall conformational stability. \u003cstrong\u003eb)\u003c/strong\u003e Rg profiles reflect global compactness of the complexes. \u003cstrong\u003ec)\u003c/strong\u003e SASA shows exposure of surface residues and interface accessibility. \u003cstrong\u003ed)\u003c/strong\u003e RMSF analysis highlights localized flexibility, with the light chain exhibiting higher fluctuations in selected mutants (M1, M3, M4), suggesting adaptive interface plasticity without global destabilization. These metrics reveal that epitope redistribution in mutants maintains overall complex integrity while enabling subtle conformational adjustments.\u003c/p\u003e","description":"","filename":"floatimage52.png","url":"https://assets-eu.researchsquare.com/files/rs-9535817/v1/2b29932198d3da858c85cd3d.png"},{"id":108014193,"identity":"82c6069e-6de8-4b65-848d-1925d9f98644","added_by":"auto","created_at":"2026-04-28 13:26:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3374848,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9535817/v1/0745a02a-a851-428b-9d16-e17c3eddce6b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mutation-driven reorganisation of E2–RRV12 antibody binding interfaces across Ross River virus variants: A computational study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRoss River virus (RRV) is the most prevalent arthritogenic alphavirus in Australia and a leading cause of epidemic polyarthritis in the Asia\u0026ndash;Pacific region [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Thousands of cases are reported annually in Australia, with outbreaks imposing significant healthcare and socioeconomic burdens [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Clinically, infection is characterized by fever, rash, myalgia, and often persistent polyarthritis that can last months to years, significantly impairing quality of life [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. RRV is transmitted by mosquitoes, primarily \u003cem\u003eAedes\u003c/em\u003e and \u003cem\u003eCulex\u003c/em\u003e species, and maintained in complex zoonotic transmission cycles involving marsupial reservoir hosts [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite decades of surveillance efforts, recurrent outbreaks underscore persistent gaps in our understanding of viral evolution, tissue tropism, and immune-mediated disease mechanisms.\u003c/p\u003e \u003cp\u003eRRV is a positive-sense single-stranded RNA virus (~\u0026thinsp;11.8\u0026ndash;11.9 kb) within the family \u003cem\u003eTogaviridae\u003c/em\u003e. The genome is organized into two major open reading frames separated by a conserved 26S subgenomic RNA promoter, encoding non-structural proteins (nsP1\u0026ndash;nsP4) for replication and structural proteins (C, E3, E2, 6K/TF, and E1) that mediate virion assembly and entry (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Among these, the E2 glycoprotein is the principal surface-exposed envelope protein mediating host cell attachment and receptor engagement [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. It interacts with cellular receptors such as MXRA8, along with additional attachment factors including heparan sulfate proteoglycans, thereby facilitating clathrin-mediated endocytosis and productive infection. The efficiency and specificity of receptor interactions influence cellular susceptibility, replication efficiency, and viral dissemination (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Beyond entry, this protein plays a central role in determining tissue tropism and inflammatory pathology. Receptor-dependent internalization permits infection of musculoskeletal and immune cell populations, including macrophages, synovial fibroblasts, and skeletal muscle cells. Infection of these cells triggers the release of pro-inflammatory mediators such as TNF-α, IL-6, and MCP-1, promoting immune cell recruitment and amplification of local inflammatory signalling. The resulting cytokine-driven cascade contributes to synovial inflammation, joint swelling, pain, and the chronic arthralgia characteristic of RRV disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, E2 is the principal target of neutralizing antibodies and therefore occupies a central position at the interface between viral fitness and host immunity. Following antigen presentation and CD4⁺ T cell\u0026ndash;dependent B cell activation, E2-specific antibodies block receptor binding and promote Fc receptor\u0026ndash;mediated viral clearance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, evolutionary pressure arising from host immune responses and vector\u0026ndash;host transmission dynamics may favour amino-acid substitutions that enhance receptor affinity, optimize fusion efficiency, or diminish antibody recognition [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Such adaptive mutations may simultaneously modulate infectivity, tissue tropism, and immune escape, yet their mechanistic consequences remain incompletely defined. While environmental and vector-related factors are established drivers of RRV transmission [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], the role of viral genetic adaptation in shaping outbreak dynamics remains incompletely understood. Given that the E2 glycoprotein is the primary mediator of receptor engagement and antibody recognition, it is a likely target for adaptive selection. However, the lack of a comprehensive framework linking naturally occurring E2 substitutions to functional changes in viral entry and immune evasion limits our ability to interpret viral evolution or inform vaccine design.\u003c/p\u003e \u003cp\u003eTo address this gap, this study integrates spatiotemporal surveillance data with E2 sequence variation through phylogenetic, structural, and mutational analyses. Computational modelling, antibody docking, and molecular dynamics simulations were used to assess how substitutions may alter epitope accessibility and antibody interactions. Linking viral evolution to functional immune outcomes may provide mechanistic insight into RRV pathogenicity and help inform vaccine development against arthritogenic alphaviruses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSpatiotemporal distribution of RRV in Australia\u003c/h2\u003e \u003cp\u003eData on laboratory confirmed RRV cases in Australia from 1991 to 2025 were obtained from the National Notifiable Diseases Surveillance System (NNDSS) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nindss.health.gov.au/pbi-dashboard/\u003c/span\u003e\u003cspan address=\"https://nindss.health.gov.au/pbi-dashboard/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Only complete records including year of notification and state/territory of residence were included. Annual case counts were aggregated by year and by state/territory. Temporal trends were visualized using line graphs and bar charts. The geographic distribution of reported cases was mapped using administrative boundaries of Australian states and territories. Maps were created to illustrate regional differences in reported cases, while line and bar plots depicted temporal trends and relative incidence across states.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSequence retrieval, dataset curation and phylogenetic analysis\u003c/h3\u003e\n\u003cp\u003eComplete genome sequences of RRV were retrieved from the National Center for Biotechnology Information (NCBI) Virus database in October 2025. A total of 85 complete genomes originating from Australia were included. Sequences were filtered to exclude incomplete genomes, partial structural polyprotein regions, truncated non-structural proteins, or entries containing ambiguous nucleotide calls. Only high-quality, full-length coding sequences of the structural polyprotein (Capsid\u0026ndash;E3\u0026ndash;E2\u0026ndash;6K\u0026ndash;E1) were retained to ensure robust evolutionary and structural analyses. These sequences were aligned using the MUSCLE algorithm in MEGA X with default parameters [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The best-fit amino acid substitution model for phylogenetic analysis was determined using the model selection function in MEGA X based on the lowest Bayesian Information Criterion (BIC) score. Maximum-likelihood (ML) phylogenetic reconstruction was then performed using the selected substitution model, and branch support was assessed with 1,000 bootstraps replicates. The resulting phylogeny was used to characterize lineage clustering among Australian RRV isolates, providing a framework for downstream analyses of E2 variation. The E2 envelope glycoprotein was selected as the primary focus due to its critical roles in receptor engagement and antibody recognition. The E2 coding region (amino acids 348\u0026ndash;750 of the canonical structural polyprotein) was extracted from each genome based on the annotation of the earliest available Australian isolate (1959 reference strain) and used for subsequent mutation, structural, and antibody-binding analyses.\u003c/p\u003e\n\u003ch3\u003eMultiple sequence alignment and mutation profiling\u003c/h3\u003e\n\u003cp\u003eThe earliest RRV isolate in the dataset was designated as the reference E2 sequence (WT). Amino-acid substitutions were identified through multiple sequence alignment in MEGA X [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For each isolate, total non-synonymous substitutions relative to the reference were enumerated. Isolates were ranked according to mutation burden, and the five isolates harbouring the highest number of E2 substitutions were selected for downstream structural and computational analyses.\u003c/p\u003e\n\u003ch3\u003eFunctional impact prediction of E2 substitutions\u003c/h3\u003e\n\u003cp\u003eAll identified substitutions in the five selected isolates were evaluated for potential effects on protein stability and structural function using six complementary computational algorithms: I-Mutant 2.0 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], MUpro [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], PolyPhen-2 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], CUPSAT [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], DynaMut2 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and SIFT [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Default or algorithm-recommended parameters were applied. Mutations predicted by multiple tools to decrease structural stability or impair protein function were prioritized for structural interpretation and dynamic analysis.\u003c/p\u003e\n\u003ch3\u003eWild structure and mutant variant modelling\u003c/h3\u003e\n\u003cp\u003eThree-dimensional models of the wild-type (WT) E2 glycoprotein and the five highly mutated variants were generated using SWISS-MODEL [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The closest available alphavirus E2 structural template with high sequence identity and structural resolution was selected for modelling.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eWild type and mutant E2 glycoprotein docking interaction with antibodies\u003c/h2\u003e \u003cp\u003eAs no experimentally resolved structure of an RRV-specific neutralizing antibody was previously available, we used Fab CHK-265 (also named as RRV-12 Fab), a broadly neutralizing human monoclonal antibody related to CHK-265 as a structural surrogate for our docking studies. Powell \u003cem\u003eet al.\u003c/em\u003e (2020) identified RRV-12 as a Fab that binds a conserved epitope in the B domain of the RRV E2 glycoprotein, preventing attachment to the alphavirus receptor MXRA8 and potently neutralizing RRV infection in vitro and in vivo [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The cryo-electron microscopy structure of this antibody in complex with RRV (PDB ID: 6VYV) therefore provides a valid proxy to study antibody\u0026ndash;E2 interactions and to assess the impact of naturally occurring E2 mutations on binding, despite the absence of a strictly RRV-specific Fab structure. Antibody\u0026ndash;antigen docking between the Fab RRV-12 and each E2 structure (WT and variants) was performed using the HDOCK server [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Docked complexes were ranked by predicted binding energy scores. Interface residues, hydrogen-bond interactions, and epitope\u0026ndash;paratope contacts were analysed using Discovery Studio Visualizer [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] to assess the structural consequences of E2 mutations on antibody engagement.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMolecular dynamics simulations\u003c/h3\u003e\n\u003cp\u003eTo assess the dynamic stability and conformational behaviour of the antibody\u0026ndash;E2 complexes, molecular dynamics (MD) simulations were performed using YASARA Dynamics (v19.12.4) with the AMBER14 force field. Initially, the docked complexes were prepared by optimizing hydrogen bond networks and removing structural artifacts. The system was solvated using a TIP3P water model with a density of 0.997 g/mL, temperature set at 25\u0026deg;C, and pressure at 1 atm, employing steepest descent minimization to ensure proper hydration [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Physiological conditions were applied by neutralizing the system and adding 0.9% NaCl at pH 7.4 and a temperature of 310 K, approximating normal human body conditions (37\u0026deg;C) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This ionic environment supports realistic protein stability and interactions with ligands [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. A time step of 1.25 fs was used, and long-range electrostatics were calculated using the particle mesh Ewald (PME) method with an 8 \u0026Aring; cutoff [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Trajectories were saved every 100 ps over a total simulation of 100 ns. Post-simulation analyses included root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), solvent-accessible surface area (SASA), and radius of gyration (Rg) dynamics to evaluate complex stability and conformational changes. Comparative analyses between WT and mutated complexes were performed to quantify mutation-driven changes in structural stability and antibody-binding dynamics.\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSpatiotemporal dynamics of RRV infections in Australia\u003c/h2\u003e \u003cp\u003eAnalysis of Australian RRV notification data (1991\u0026ndash;2025) shows substantial inter-annual variability with episodic epidemic peaks. No cases were reported in 1991\u0026ndash;1992, followed by a rise in the mid-1990s, peaking at 7,765 cases in 1996 and 6,612 in 1997. Notifications remained elevated in the early 2000s (~\u0026thinsp;3,000\u0026ndash;5,600 cases/year). A pronounced epidemic occurred between 2014\u0026ndash;2017, with the highest peak in 2015 (9,532 cases), followed by another surge in 2017 (6,933 cases), likely reflecting favourable ecological and climatic conditions. A further increase occurred in 2020 (6,367 cases), with moderate endemic transmission persisting from 2021\u0026ndash;2025 (1,711\u0026ndash;3,375 cases/year) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, \u003cb\u003esee supplementary file\u003c/b\u003e). These trends highlight RRV\u0026rsquo;s cyclical, outbreak-prone dynamics and underscore the importance of continuous surveillance and early warning systems.\u003c/p\u003e \u003cp\u003eState-level notifications reveal notable geographic heterogeneity. Queensland bore the highest cumulative burden (69,235 cases), showing repeated epidemic peaks in 1996, 2015, 2020, and 2024, reflecting tropical/subtropical vector-favorable conditions. New South Wales (26,018 cases) and Western Australia (21,589 cases) showed substantial but variable transmission, with notable surges during 1996\u0026ndash;1997, 2015\u0026ndash;2017, and 2020. Victoria (11,610 cases) had episodic spikes in 2011 and 2017, while Northern Territory (6,769 cases) and South Australia (6,844 cases) exhibited moderate, intermittent activity. Tasmania (754 cases) and the Australian Capital Territory (242 cases) consistently reported low incidence, constrained by climate and ecology (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, \u003cb\u003esee supplementary file\u003c/b\u003e). These data are also illustrated in the accompanying map (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec), which visually highlights the uneven spatial distribution of RRV cases across Australia and showed that Queensland is the national hotspot for these viruses over multiple decades. Previous studies also reported northern and coastal regions as persistent hotspots with elevated incidence rates, while inland and southern areas exhibited lower and more variable transmission patterns [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. National outbreaks during major epidemic years (1996\u0026ndash;1997, 2015\u0026ndash;2017, 2020) were interspersed with lower endemic transmission phases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eHost distribution and geographic origin of complete RRV genome sequences\u003c/h2\u003e \u003cp\u003eA total of 85 complete RRV structural polyprotein sequences were analysed, representing isolates from multiple hosts and geographic regions across Australia. The majority originated from mosquito vectors (77%, n\u0026thinsp;=\u0026thinsp;66), followed by human host (17%, n\u0026thinsp;=\u0026thinsp;15), with smaller contributions from avian (4%, n\u0026thinsp;=\u0026thinsp;3) and wallaby (2%, n\u0026thinsp;=\u0026thinsp;2) sources (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed), reflecting the multi-host ecology of RRV transmission. Geographically, New South Wales accounted for the largest proportion of sequences (43%), followed by Queensland (24%) and the Northern Territory (12%), while Victoria, Tasmania, and other regions contributed fewer isolates (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEvolutionary divergence of the E2 glycoprotein and its correlation with epidemic waves\u003c/h2\u003e \u003cp\u003eStructural polyprotein based phylogenetic analysis of RRV strains in Australia includes the oldest available reference sample, AYI50356.1 (WT), an \u003cem\u003eOchlerotatus vigilax\u003c/em\u003e isolate from 1959 \u003cb\u003e(see supplementary Fig. S1\u003c/b\u003e). This foundational lineage shows close temporal and genetic relationships with other early samples, including another 1959 \u003cem\u003eO. vigilax\u003c/em\u003e isolate (ACV67002.1) and mid-1960s avian isolates such as \u003cem\u003eMicroeca fascinans\u003c/em\u003e and \u003cem\u003ePoephila personata\u003c/em\u003e. Over several decades, the virus has demonstrated significant host plasticity, circulating among primary mosquito vectors including \u003cem\u003eCulex annulirostris\u003c/em\u003e and various \u003cem\u003eOchlerotatus\u003c/em\u003e species and vertebrate reservoirs such as marsupials (\u003cem\u003eNotamacropus agilis\u003c/em\u003e). Phylogenetic clustering further reveals repeated spillover into human populations (\u003cem\u003eHomo sapiens\u003c/em\u003e), with documented infections spanning from 1971 to 2017. The overall tree topology, with a scale of 0.01, illustrates gradual genetic divergence from its 1959 origins into a diverse range of contemporary ecological niches across Australia. Notably, recent isolates from 2017\u0026ndash;2018, including the human isolate AZF99254.1, cluster at the termini of extended lineages, indicating that while the virus has diverged significantly from its ancestral state, it remains ecologically stable and continues to utilize a wide array of mosquito vectors, including \u003cem\u003eO. camptorhynchus\u003c/em\u003e and \u003cem\u003eC. annulirostris\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eTemporal analysis of E2 glycoprotein sequences (1959\u0026ndash;2018) revealed a progressive accumulation of naturally occurring substitutions, with early isolates (1960s\u0026ndash;1980s) showing limited variation and post-1990 strains consistently harbouring core substitutions (Y5H, I54L, R238K, frequently D119N). Additional substitutions emerging from the mid-2000s onward (e.g., T371I, A356T, M363I, Y283H, A376T) were temporally coincident with several major epidemic periods, particularly the 2014\u0026ndash;2017 outbreak (\u003cb\u003eTable S1\u003c/b\u003e). Although causality cannot be established, the temporal clustering of expanded E2 mutational profiles with high-incidence years is consistent with reports in other arboviruses showing that gradual sequence diversification can influence viral fitness and host adaptation over time, even under predominant purifying selection [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Similar genome-wide lineage turnover has been documented in RRV and linked to historical epidemic patterns [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, climatic and ecological factors including rainfall, temperature and vector abundance remain the primary drivers of transmission variability across Australia [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. These findings suggest that while environmental conditions largely determine outbreak magnitude, viral evolution within E2 may act as a contributory factor by subtly modulating antigenic properties and transmission dynamics over multi-year cycles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStructural architecture, compositional features and mutational landscape of the RRV E2 glycoprotein\u003c/h2\u003e \u003cp\u003eTwo-dimensional structural annotation of the RRV E2 glycoprotein with a conserved length of 403 amino acids across all analysed strains, revealed the canonical alphavirus organization comprising a large extracellular domain enriched in α-helices and β-strands interconnected by flexible coil regions, followed by a single C-terminal transmembrane helix and a short cytoplasmic tail (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The extracellular region displayed structured β-sheet and helical cores interspersed with surface-exposed loops and short disordered segments, consistent with a requirement for conformational plasticity in receptor engagement and antigenic recognition [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The hydrophobic transmembrane segment at the C terminus appeared structurally conserved, supporting its essential role in membrane anchoring and virion assembly [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Amino acid composition analysis further demonstrated enrichment of threonine (9.20%), alanine (8.91%), proline (7.68%) and glycine (6.71%), whereas tryptophan (1.24%), methionine (1.49%) and asparagine (1.88%) were least represented \u003cb\u003e(see supplementary file Table S2\u003c/b\u003e). This compositional bias may reflect broader structural constraints associated with protein stability and adaptability. However, as structural flexibility is inherently a local property, global amino acid composition alone does not permit direct inference regarding specific flexible or surface-exposed region.\u003c/p\u003e \u003cp\u003eComparative analysis of 85 isolates identified 335 naturally occurring amino acid substitutions within E2, corresponding to an average of ~\u0026thinsp;3.9 substitutions per isolate and a per-site mutation frequency of 0.0098 (~\u0026thinsp;0.98%) across the 403-residue glycoprotein (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Five isolates harbouring the highest number of substitutions were selected for detailed structural assessment (\u003cb\u003eSupplementary Table S2\u003c/b\u003e) and were designated M1 (formerly WAB51748.1; seven substitutions), M2 (WAB51716.1; seven substitutions), M3 (WAB51730.1; six substitutions), M4 (WAB51830.1; five substitutions) and M5 (WAB51818.1; five substitutions) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Substitutions were distributed across both N-terminal and central regions of the protein. M1 contained Y5H, V19I, I54M, A105T, R238K, E289V and H291Q (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee); M2 carried Y5H, I54L, D119N, R238K, A356T, M363I and T371A (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef); M3 harboured Y5H, I54L, R238K, Y283H, E309V and A376T (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg); M4 included Y5H, M32K, I54L, R238K and L273F (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh); and M5 contained Y5H, I54L, K117M, R238K and M363V (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ei). Notably, Y5H and R238K were conserved across all five variants, indicating potential functional relevance. Several additional substitutions are located in regions corresponding to predicted surface-exposed loop regions of the E2 protein based on structural modelling, which are commonly implicated in receptor interaction and antigenicity. Based on the identified substitutions, three-dimensional structures of the five mutant variants (M1\u0026ndash;M5) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee-i) and the wild-type E2 protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) were generated using homology modelling with SWISS-MODEL to evaluate antigen-antibody interaction for potential alterations associated with the observed mutations. The predicted 3D structures revealed that recurrent substitutions, including Y5H, I54L/M and R238K, localize to structurally informative regions with potential influence on folding stability, surface accessibility and interaction interfaces with host receptors or neutralizing antibodies. These findings define the structural framework and mutational spectrum of the RRV E2 glycoprotein, providing a foundation for subsequent stability and dynamic analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStructural and functional impact of recurrent E2 protein mutations\u003c/h2\u003e \u003cp\u003eTo predict the structural and functional impact of recurrent amino acid substitutions in the RRV E protein, we analysed the most frequently observed mutations using complementary in silico prediction tools, including I-Mutant, Mupro, PolyPhen-2, CUPSAT, and DynaMut2 (\u003cb\u003eSupplementary Table S3\u003c/b\u003e). Substitutions such as Y5H and R238K, conserved across the five most prevalent mutant isolates, were predominantly predicted to destabilize the protein. Specifically, Y5H consistently reduced stability (I-Mutant: \u0026minus;\u0026thinsp;0.98; Mupro: \u0026minus;\u0026thinsp;1.49) and was classified as \u0026ldquo;probably damaging\u0026rdquo; by CUPSAT and DynaMut2, suggesting potential alterations in folding and surface exposure. In contrast, R238K exhibited context-dependent effects, with I-Mutant and Mupro predicting decreased stability (\u0026ndash;1.09, \u0026minus;\u0026thinsp;1.23), whereas DynaMut2 indicated stabilizing influences in certain structural environments (1.71), consistent with a role in modulating local conformational dynamics without compromising global envelope integrity. Other substitutions displayed a spectrum of predicted effects. For example, V19I and A105T were generally destabilizing, whereas I54M, D119N, M363V/I, and E289V were predicted to enhance stability according to I-Mutant and Mupro, although functional predictors (PolyPhen-2, CUPSAT) suggested these changes were tolerated or \u0026ldquo;possibly damaging.\u0026rdquo; Mutations localized to surface-exposed loops, such as L273F, Y283H, T371A, and A376T, were variably predicted to affect protein stability and function, potentially influencing receptor interactions or antigenicity. Mutational studies further underscore the biological relevance of E2 variation beyond sequence divergence. A single Y18H substitution in the E2 ectodomain markedly attenuates disease in a mouse model of alphavirus-induced musculoskeletal inflammation, demonstrating the capacity of specific mutations to alter viral fitness and pathogenesis [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Similarly, selection of RRV variants bearing substitutions in the E2 B domain (e.g., K189Q, D214A) under antibody pressure highlights the role of E2 mutations in shaping antigenic surfaces and neutralization sensitivity in vivo [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Functional analyses of E2 glycan mutants (N200Q, N262Q) reveal that removal of select glycans attenuates complement-mediated pathology, emphasizing the contribution of E2 structural features and post-translational modifications to immune-driven disease outcomes [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Overall, these observations indicate that recurrent substitutions in the RRV E protein impart diverse structural consequences, modulating local folding, surface accessibility, and potential host interactions while largely preserving global envelope architecture. This integrative perspective illuminates how RRV balances structural stability with antigenic flexibility to facilitate host adaptation and immune evasion.\u003c/p\u003e \u003cp\u003e \u003cb\u003eComparative binding analysis of wild-type and mutant RRV E2 variants with the Fab RRV-12 neutralizing antibody\u003c/b\u003e \u003c/p\u003e \u003cp\u003eComputational docking analysis revealed measurable differences in antibody\u0026ndash;antigen interactions between the wild-type (WT) RRV E2 protein and selected mutant variants. The WT E2\u0026ndash;Fab RRV-12 complex demonstrated the strongest predicted binding affinity, with a global binding energy of \u0026minus;\u0026thinsp;301.31 kcal/mol, serving as the reference for neutralization potential (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among the mutant variants, M2 (\u0026ndash;299.95 kcal/mol) and M3 (\u0026ndash;299.06 kcal/mol) retained comparable binding energies to WT despite sequence variation (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In contrast, M1 (\u0026ndash;295.53 kcal/mol) and M4 (\u0026ndash;291.83 kcal/mol) showed a modest reduction in predicted affinity. Overall, the relatively narrow binding energy range (\u0026ndash;301 to \u0026minus;\u0026thinsp;291 kcal/mol) suggests that Fab RRV-12 maintains broadly conserved binding potential across circulating variants.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eBinding energies and interacting residues of wild-type and mutant RRV E2 proteins in complex with the Fab RRV-12 antibody.\u003c/b\u003e Binding free energies (kcal mol⁻\u0026sup1;) and interacting amino acid residues at the E2\u0026ndash;Fab RRV-12 interface is presented for the wild-type (WT) and five mutant variants (M1\u0026ndash;M5). All listed contact residues represent hydrogen bond interactions identified at the antigen\u0026ndash;antibody interface. Here, E, I, and M are antigen E2 glycoprotein, RRV-12 antibody heavy chain, and light chain respectively.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplexes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBinding energy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInteracting amino acid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComplexes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBinding energy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInteracting amino acid\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWT\u0026thinsp;+\u0026thinsp;RRV-12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-301.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Asp161 (E)-Gln1 (I)\u003c/p\u003e \u003cp\u003e- Tyr16(E)-Gly26(I)\u003c/p\u003e \u003cp\u003e- Arg165(E)- Thyr32 (I)\u003c/p\u003e \u003cp\u003e- Tyr5(E)-Ala76(I)\u003c/p\u003e \u003cp\u003e- Cys212(E)-Tyr100(I)\u003c/p\u003e \u003cp\u003e- His213(E)- Tyr106(I)\u003c/p\u003e \u003cp\u003e- Arg25(E)-Ser160(I)\u003c/p\u003e \u003cp\u003e- Asn187(E)-Asn273(M)\u003c/p\u003e \u003cp\u003e- Tyr186(E)-Arg274(M)\u003c/p\u003e \u003cp\u003e- Gln234(E)-Ser389(M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eM3\u0026thinsp;+\u0026thinsp;RRV-12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-299.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e- Asp161(E)-Gln1(I)\u003c/p\u003e \u003cp\u003e- Tyr16(E)-Gly26(I)\u003c/p\u003e \u003cp\u003e- Arg165(E)-Tyr32(I)\u003c/p\u003e \u003cp\u003e- His5(E)-Ala76(I)\u003c/p\u003e \u003cp\u003e- Cys212(E)-Tyr100(I)\u003c/p\u003e \u003cp\u003e- His213(E)-Tyr106(I)\u003c/p\u003e \u003cp\u003e- Arg25(E)-Ser160(I)\u003c/p\u003e \u003cp\u003e- Asn187(E)-Asn-273(M)\u003c/p\u003e \u003cp\u003e- Tyr186(E)-Arg274(M)\u003c/p\u003e \u003cp\u003e- Gln234(E)-Ser389(M)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eM1\u0026thinsp;+\u0026thinsp;RRV-12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-295.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Asp161 (E)-Gln1 (I)\u003c/p\u003e \u003cp\u003e- Tyr16(E)-Gly26(I)\u003c/p\u003e \u003cp\u003e- His5(E)-Ala76(I)\u003c/p\u003e \u003cp\u003e- Cys212(E)-Tyr100(I)\u003c/p\u003e \u003cp\u003e- His213(E)- Tyr106(I)\u003c/p\u003e \u003cp\u003e- Glu27(E)-Ser160(I)\u003c/p\u003e \u003cp\u003e- Arg237(E)-Ser165(I)\u003c/p\u003e \u003cp\u003e- Glu153(E)-Ser165(I)\u003c/p\u003e \u003cp\u003e- Gln234(E)-Pro260(M)\u003c/p\u003e \u003cp\u003e- Tyr186(E)-Arg274(M)\u003c/p\u003e \u003cp\u003e- Asn194(E)-Arg274(M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eM4\u0026thinsp;+\u0026thinsp;RRV-12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-291.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e- Tyr346(E)-Ile2(I)\u003c/p\u003e \u003cp\u003e- Tyr346(E)-Leu4(I)\u003c/p\u003e \u003cp\u003e- Glu340(E)-Arg9(I)\u003c/p\u003e \u003cp\u003e- Glu331(E)-Gln109(I)\u003c/p\u003e \u003cp\u003e- Glu268(E)-Thr78(I)\u003c/p\u003e \u003cp\u003e- His335(E)-Leu156(I)\u003c/p\u003e \u003cp\u003e- His335(E)-Thr169(I)\u003c/p\u003e \u003cp\u003e- His339(E)-Lys387(M)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eM2\u0026thinsp;+\u0026thinsp;RRV-12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-299.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Leu365(E)-Arg9(I)\u003c/p\u003e \u003cp\u003e- Arg380(E)-Asn88(I)\u003c/p\u003e \u003cp\u003e- Arg380(E)-Thr114(I)\u003c/p\u003e \u003cp\u003e- Cys372(E)-Ala172(I)\u003c/p\u003e \u003cp\u003e- Cys403(E)-Gln175(I)\u003c/p\u003e \u003cp\u003e- Glu340(E)-Asn190(I)\u003c/p\u003e \u003cp\u003e- His339(E)-Asn190(I)\u003c/p\u003e \u003cp\u003e- Leu366(E)-His262(M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eM5\u0026thinsp;+\u0026thinsp;RRV-12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-298.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e- Arg380(E)-Asn88(I)\u003c/p\u003e \u003cp\u003e- Arg380(E)-Thr114(I)\u003c/p\u003e \u003cp\u003e- Cys372(E)-Ala172(I)\u003c/p\u003e \u003cp\u003e- Cys403(E)-Gln175(I)\u003c/p\u003e \u003cp\u003e- Glu340(E)-Asn190(I)\u003c/p\u003e \u003cp\u003e- His339(E)-Asn190(I)\u003c/p\u003e \u003cp\u003e- Leu366(E)-His262(M)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDetailed interface comparison revealed that the WT E2\u0026ndash;RRV-12 complex is characterized by hydrogen bond interactions primarily within the N-terminal and central regions of E2 involving residues Tyr5, Tyr16, Arg25, Asp161, Arg165, Asn187, Tyr186, Cys212, His213 and Gln234 of E2. These residues interact with Fab RRV-12 antibody residues Gln1, Gly26, Tyr32, Ala76, Tyr100, Tyr106, Ser160, Asn273, Arg274 and Ser389, which defining a structurally coordinated conformational epitope recognized by the RRV-12 antibody (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). In M3, the interaction pattern remained largely conserved relative to WT, with His5 replacing Tyr5 while maintaining overall same binding footprint, indicating preservation of the native epitope architecture (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). In M1 exhibited partial reorganization of the interface, with additional contacts involving Glu27, Arg237, Glu153 and Asn194, suggesting local reshaping of the antibody-binding surface while retaining several core residues such as His5, Cys212, His213 and Tyr186 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). In contrast, M2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec) and M5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef), displayed altered docking poses characterised by a shift in dominant contact regions toward the C-terminal region of E2, involving residues Arg380, Cys372, Cys403, Glu340, His339 and Leu366. Notably, these variants also exhibited differences in the overall orientation of the antibody relative to the antigen compared to the WT complex, suggesting that the observed changes may reflect alternative docking conformations rather than simple local shifts in contact residues. The M4 variant displayed the most distinct binding profile, with interactions concentrated around residues Tyr346, Glu331, Glu268, His335 and His339 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). However, the predicted interaction interface in this variant differs from the WT binding configuration and does not fully recapitulate the characteristic interaction pattern observed in the reference complex, indicating a potentially lower-confidence or alternative docking pose. Overall, integration of binding energy and interface analyses suggests that WT and M3 maintain a conserved binding interface, M1 shows partial interface reorganisation and M2, M4 and M5 displayed altered binding. However, given the inherent limitations of computational docking, the possibility of pose-dependent artifacts cannot be excluded, and the results should be interpreted as predictive rather than definitive.\u003c/p\u003e \u003cp\u003eThese findings indicate that naturally occurring substitutions can remodel the spatial or altered configuration of the antibody-binding surface without abolishing interaction capacity, consistent with emerging structure-function relationships in alphaviruses where sequence variation reshapes immunogenic topography. Broadly neutralizing monoclonal antibodies across arthritogenic alphaviruses predominantly target structurally conserved regions of the E2 glycoprotein, particularly within the B domain, and substitution of key residues within these epitopes markedly alters antibody binding profiles and neutralization potency [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The preservation of crucial interfacial residues and comparable binding energetics between wild-type and the M3 variant suggests maintenance of antigenic stability, supporting the classification of RRV-12 as a broadly neutralizing antibody capable of recognizing diverse E2 variants, as described for human mAbs that engage conserved footprints on RRV, Chikungunya and Mayaro virus E2 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In contrast, the epitope redistribution observed in M1, M2, M4 and M5 especially the altered interaction profile in M4 demonstrates that substitutions can shift or altered antibody contact sites and subtly reshape binding architecture (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This pattern is consistent with alphavirus escape variants identified under antibody pressure, where mutations in E2 domain B residues reduce neutralization sensitivity without abolishing overall structural integrity [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Although interactions remain energetically favourable, spatial reorganization of surface-exposed regions may alter antibody accessibility, orientation, and neutralization efficiency, facilitate immune escape and viral evolution while preserve key determinants of attachment and entry. From a translational perspective, the relative conservation of binding profiles suggests that vaccines based on wild-type E2 antigens could provide cross-protective immunity against circulating variants; nevertheless, continued surveillance of epitope-altering substitutions is essential, as their accumulation in major antigenic sites can progressively diminish recognition and promote immune evasion in alphaviruses [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of dynamic stability and flexibility of antigen\u0026ndash;antibody complexes\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003eRMSD analysis and structural stability of antigen\u0026ndash;antibody complexes\u003c/h2\u003e \u003cp\u003eThe average backbone RMSD values for all antigen\u0026ndash;antibody complexes ranged from 0.34 to 1.67 \u0026Aring; [WT (0.34), M2 (1.06), M3 (1.48), M1 (1.16), M5 (1.67), and M4 (1.01)], indicating preserved global structural stability with only moderate conformational adaptation in selected mutants. All trajectories showed an initial RMSD increase during equilibration (0\u0026ndash;20 ns) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). The initial RMSD increase reflects relaxation from the docked conformation and solvent accommodation during equilibration, a common feature of antibody\u0026ndash;antigen MD simulations rather than structural destabilization [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. During the post- equilibration phase (20\u0026ndash;100 ns), RMSD profiles stabilized with minimal fluctuations and no sustained upward drift, abrupt spikes, or signs of dissociation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). The maintenance of RMSD values below 2 \u0026Aring; across all systems suggests that global antigen\u0026ndash;antibody architecture remained stable during the simulation. The WT complex exhibited the lowest RMSD, serving as a stable structural benchmark. Mutants displayed modestly elevated RMSD values, consistent with localized conformational adjustments within the epitope\u0026ndash;paratope interface rather than large-scale structural perturbations. Such adaptive micro-rearrangements are characteristic of alphavirus E2 glycoprotein flexibility, particularly within surface-exposed B-domain regions known to accommodate antibody binding while maintaining functional constraints required for receptor engagement and viral entry [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Structural analyses of arthritogenic alphaviruses have similarly demonstrated that neutralizing antibodies can tolerate limited epitope plasticity without loss of overall complex integrity, reflecting a balance between immune pressure and envelope protein stability [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. These findings indicate that E2 substitutions enhance interface-level adaptability without destabilizing the antigen\u0026ndash;antibody complex. This structural plasticity permits subtle shifts in binding orientation and contact networks that may modulate neutralization efficiency while preserving antibody engagement and envelope integrity. Such E2 mutations remodel antigenic surfaces without compromising assembly or entry functions. So, this MD data suggest that recurrent E2 mutations fine-tune interaction dynamics rather than disrupt global architecture, supporting a mechanism of partial immune adaptation within structurally constrained viral envelopes.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eRadius of gyration (Rg) analysis and global compactness of antigen\u0026ndash;antibody complexes\u003c/h2\u003e \u003cp\u003eThe average backbone Rg values demonstrated that all antigen\u0026ndash;antibody complexes maintained overall structural compactness throughout the 100 ns simulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Mean Rg values were WT (3.4661 \u0026Aring;), 23 (5.0758 \u0026Aring;), M3 (3.7392 \u0026Aring;), M1 (3.8496 \u0026Aring;), M5 (4.3484 \u0026Aring;), and M4 (1.7574 \u0026Aring;). Despite quantitative differences among variants, Rg values remained within relatively narrow and stable ranges over time, with no sudden expansions or abrupt fluctuations indicative of structural collapse or unfolding (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). These findings suggest that both WT and mutant antigen\u0026ndash;antibody complexes preserved stable global folding, indicating that the investigated mutations do not induce large-scale structural destabilization of the RRV E protein during antibody engagement. Trend analysis revealed that from the start of the simulation to approximately 50 ns, all complexes except M5 displayed stable Rg profiles, followed by a slight decreasing trend suggesting gradual structural relaxation and compaction after initial equilibration (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Complexes M2 and M4 maintained overall stability with temporal patterns comparable to WT and other mutants; however, their consistently higher Rg values indicate comparatively less compact structural assemblies throughout the simulation [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. This may reflect altered epitope orientation or expanded interfacial arrangements resulting from alternative binding pockets identified during docking analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec \u003cb\u003e\u0026amp; e\u003c/b\u003e). In contrast, M1 and M3 exhibited Rg values closer to the WT profile, indicating WT-like compactness and structural organization. A distinct Rg trajectory was observed for M5. This complex initially followed a stable trend similar to other systems up to ~\u0026thinsp;10 ns, followed by a transient increase between 10\u0026ndash;20 ns, likely reflecting early conformational rearrangement associated with alternative binding pocket accommodation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Subsequently, the Rg gradually decreased until ~\u0026thinsp;60 ns and stabilised thereafter, suggesting successful structural adaptation and attainment of a compact, energetically favourable conformation during later simulation stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). The consistently stable Rg values across all complexes indicate that mutations and binding pocket shifts do not compromise the overall compactness or structural integrity of the antigen\u0026ndash;antibody assemblies. The absence of significant Rg expansion supports the conclusion that mutations do not promote unfolding or loosening of the E protein structure upon antibody binding [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Comparable stability profiles between WT-like complexes and mutants engaging alternative epitopes suggest that distinct binding modes converge toward similarly stable structural architectures [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. This reflects evolutionary constraints on the RRV E protein, where surface-level adaptations allow modified antigenic interactions while preserving the conserved global fold essential for viral fitness and immune recognition [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Consequently, differential epitope usage appears to modulate local conformational landscapes without disrupting the structural robustness of the immune complex.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSolvent-accessible surface area (SASA) analysis and antigen exposure dynamics\u003c/h2\u003e \u003cp\u003eSASA profiles of all antigens\u0026ndash;antibody complexes remained largely stable throughout the 100 ns simulation period, displaying only minor fluctuations that are consistent with normal dynamic breathing motions of protein complexes. Mean SASA values were WT (461.7465 \u0026Aring;\u003csup\u003e2\u003c/sup\u003e), M2 (463.0063 \u0026Aring;\u003csup\u003e2\u003c/sup\u003e), M3 (461.6494 \u0026Aring;\u003csup\u003e2\u003c/sup\u003e), M1 (450.1827 \u0026Aring;\u003csup\u003e2\u003c/sup\u003e), M5 (474.1834 \u0026Aring;\u003csup\u003e2\u003c/sup\u003e), and M4 (469.0182 \u0026Aring;\u003csup\u003e2\u003c/sup\u003e). The relatively narrow SASA range (~\u0026thinsp;450\u0026ndash;474) across all systems indicates consistent solvent exposure and preservation of antigen surface accessibility despite the presence of mutations and altered binding modes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). Trend analysis revealed that most complexes maintained stable SASA trajectories throughout the simulation, supporting the absence of major conformational rearrangements affecting overall solvent exposure. Notably, M1 exhibited a distinct dynamic pattern after ~\u0026thinsp;50 ns, with a gradual decrease in SASA until ~\u0026thinsp;75 ns, followed by a transient increase up to ~\u0026thinsp;90 ns, and a final decline toward the end of the simulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). This behaviour likely reflects local conformational adjustments at the antigen\u0026ndash;antibody interface rather than global structural changes, suggesting transient tightening and relaxation of surface regions during complex stabilisation. These stable SASA values across both WT-like and alternative-pocket-binding mutants indicate that mutations do not induce significant epitope burial or large-scale shielding of antigenic surfaces during antibody engagement. Instead, the maintenance of comparable solvent exposure supports the concept that epitope switching arises from subtle surface reconfiguration rather than immune evasion through structural masking. This pattern contrasts with highly immune-evasive viruses that rely on extensive antigenic shielding. The preserved SASA profiles therefore suggest that RRV evolution favours partial immune modulation, where mutations enable adaptive redirection of antibody interactions while maintaining accessible antigenic surfaces and conserving overall structural integrity essential for viral function.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eRoot means square fluctuation (RMSF) analysis\u003c/h2\u003e \u003cp\u003eRoot means square fluctuation (RMSF) analysis over the 100 ns simulations revealed distinct yet coordinated flexibility patterns across the RRV E antigen (Chain-E) and the antibody heavy (Chain-I) and light (Chain-M) chains, highlighting functionally relevant structural dynamics within the antigen\u0026ndash;antibody complexes. The WT complex exhibited the lowest average RMSF (0.6091), whereas M1 (2.0061), M4 (1.7574), and M3 (1.697) showed comparatively higher flexibility, with M5 (1.3820) and M2 (1.1209) displaying moderate fluctuations. Within the 403-residue RRV E protein, moderate localized peaks (2\u0026ndash;2.5 \u0026Aring;) were observed in regions 47\u0026ndash;49, 146\u0026ndash;153, 167\u0026ndash;184, and 339\u0026ndash;360 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed), likely corresponding to surface-exposed loops and epitope regions that retain intrinsic flexibility to facilitate antibody accommodation and adaptive binding [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Pronounced fluctuation in the C-terminal segment (374\u0026ndash;403) was detected in most complexes except M5 and M2, suggesting that these antibodies confer enhanced stabilization of the membrane-proximal region (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). The antibody heavy chain exhibited moderate but controlled mobility, reflecting paratope adjustments required for stable antigen recognition without compromising structural integrity; notable fluctuations were primarily observed in M1. In contrast, the light chain displayed comparatively higher fluctuations in selected complexes (M3, M1, and M4) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed), indicating supportive conformational plasticity that may facilitate interface optimization and binding adaptability. Importantly, the absence of widespread high RMSF values across any chain indicates that the mutations do not induce global destabilization but instead promote localized flexibility within functional regions [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. These findings support a model in which RRV E protein mutations enable subtle epitope rearrangements and dynamic refinement of the binding interface while preserving overall immune complex stability, thereby allowing adaptive antigen\u0026ndash;antibody interactions without disrupting structural architecture or functional recognition.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLimitation of the study\u003c/h2\u003e \u003cp\u003eWhile this study integrates epidemiological, sequence, and structural analyses to investigate RRV E2 mutations, it is limited by the reliance on 85 sequences, the focus on a single monoclonal antibody (RRV-12), and the use of in silico predictions without experimental validation. Molecular dynamics simulations were performed on isolated antigen\u0026ndash;antibody complexes and may not fully capture the influence of full virion architecture, glycosylation, or host factors. Consequently, the effects of additional variants, alternative antibodies, and in vivo interactions remain to be explored in future studies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis integrated analysis combines epidemiological, sequence, and structural insights to reveal how recurrent substitutions in RRV E2 precisely remodel epitope landscapes and modulate antibody engagement. Mutant variants M1 and M3 maintained binding to epitopes nearly identical to WT, whereas M2, M4, and M5 exhibited redistributed antibody contacts, with M4 showing the most pronounced shift, yet overall binding stability was preserved. Such structural plasticity may support viral immune evasion and adaptive evolution while maintaining critical determinants for host-cell attachment. Critically, conserved antibody-binding regions revealed through RRV-12 interactions represent high-value targets for rational vaccine design. Exploiting these structurally constrained epitopes offers the potential to generate broadly cross-protective immunity against circulating RRV variants. These findings provide a mechanistic framework linking sequence variation, antigenic flexibility, and neutralization dynamics, establishing a foundation for next-generation, structure-guided alphavirus vaccines that anticipate viral evolution while maintaining protective efficacy.\u003c/p\u003e"},{"header":"Declarations","content":" \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003ch2\u003eFunding Declaration\u003c/h2\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eThe authors declare that no funding was received for this work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMd. Eram Hosen: Conceptualization, methodology, investigation, data curation, formal analysis, visualization, and original draft preparation. Guendouzi Abdelkrim: Molecular dynamics simulations. Paul F. Horwood: Supervision, manuscript review and editing, and critical revision of the intellectual content. Subir Sarker: Conceptualization, Supervision, project administration, and overall guidance of the study.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eSubir Sarker is the recipient of an Australian Research Council Discovery Early Career Researcher Award (grant number DE200100367) funded by the Australian Government. The Australian Government had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe data that supports the findings of this study are available in the supplementary material of this article and available to the corresponding upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZaid A, Burt FJ, Liu X, Poo YS, Zandi K, Suhrbier A, Weaver SC, Texeira MM, Mahalingam S (2021) Lancet Infect Dis 21(5):e123\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHosen ME, Dunsdon S, Sarker S (2026) Virology 617:110825\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X, Tharmarajah K, Taylor A (2017) Microbes Infect 19(11):496\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu W, Mengersen K, Dale P, Tong S (2010) Environ Entomol 39(1):88\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdullah N, Ahemad N, Aliazis K, Khairat JE, Lee TC, Abdul Ahmad SA, Adnan NAA, Macha NO, Hassan SS (2021) Viruses 13(6):1021\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo D, Tan YB, Law MCY, Jin J (2025) Annual Review of Virology 12\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePowell LA, Miller A, Fox JM, Kose N, Klose T, Kim AS, Bombardi R, Tennekoon RN, de Silva AD, Carnahan RH (2020) Cell Host Microbe 28(5):699\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKesari AS, Sharkey CM, Sanders DA (2019) Virology 529:177\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang R, Kim AS, Fox JM, Nair S, Basore K, Klimstra WB, Rimkunas R, Fong RH, Lin H, Poddar S, Crowe JE Jr., Doranz BJ, Fremont DH, Diamond MS (2018) Nature 557(7706):570\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorrison TE, Whitmore AC, Shabman RS, Lidbury BA, Mahalingam S, Heise MT (2006) J Virol 80(2):737\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMostafavi H, Tharmarajah K, Vider J, West NP, Freitas JR, Cameron B, Foster PS, Hueston LP, Lloyd AR, Mahalingam S, Zaid A (2022) PLoS Pathog 18(2):e1010185\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTappe D, Perez-Giron JV, Gomez-Medina S, G\u0026uuml;nther S, Munoz-Fontela C, Schmidt-Chanasit J (2017) Emerg Infect Dis 23(4):702\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavies JM, Cai YP, Weir RC, Rowley MJ (2000) Virology 275(1):67\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKerr PJ, Fitzgerald S, Tregear GW, Dalgarno L, Weir RC (1992) Virology 187(1):338\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHosen ME, Supti SJ, Horwood PF, Sarker S (2026) Rev Med Virol 36(1):e70082\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBi P, Hiller JE, Cameron AS, Zhang Y, Givney R (2009) Epidemiol Infect 137(10):1486\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTong S, Hu W, Nicholls N, Mackenzie J, Dale P, Wolff R et al (2007) Epidemiology 18(5):S103\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e(CDC) ACfDC (2025) National Notifiable Disease Surveillance System. Australian Government\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar S, Stecher G, Li M, Knyaz C, Tamura K (2018) Mol Biol Evol 35(6):1547\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCapriotti E, Fariselli P, Casadio R (2005) Nucleic Acids Res 33(suppl2):W306\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng J, Randall A, Baldi P (2006) Proteins: Structure. Function Bioinf 62(4):1125\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdzhubei I, Jordan DM, Sunyaev SR (2013) Current protocols in human genetics 76(1):7.20. 1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParthiban V, Gromiha MM, Schomburg D (2006) Nucleic acids research 34(suppl_2):W239\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodrigues CH, Pires DE, Ascher DB (2021) Protein Sci 30(1):60\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar P, Henikoff S, Ng PC (2009) Nat Protoc 4(7):1073\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, Heer FT, de Beer TAP, Rempfer C, Bordoli L (2018) Nucleic acids research 46(W1):W296\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePowell LA, Miller A, Fox JM, Kose N, Klose T, Kim AS, Bombardi R, Tennekoon RN, Dharshan, de Silva A, Carnahan RH, Diamond MS, Rossmann MG, Kuhn RJ, Crowe JE Jr (2020) Cell Host \u0026amp; Microbe 28(5):699\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan Y, Tao H, He J, Huang S-Y (2020) Nat Protoc 15(5):1829\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVisualizer D (2005) Accelrys software inc 649\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarrach MF, Drossel B (2014) J Chem Phys 140(17)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHosen ME, Jahan Supti S, Akash S, Rahman ME, Faruqe MO, Manirujjaman M, Acharjee UK, Gaafar A-RZ, Ouahmane L, Sitotaw B, Bourhia M, Zaman R (2023) Frontiers in Chemistry Volume 11\u0026ndash;2023\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrieger E, Dunbrack RL Jr, Hooft RW, Krieger B (2011) Assignment of protonation states in proteins and ligands: combining pKa prediction with hydrogen bonding network optimization. Springer, Computational drug discovery and design, p 405\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoqunuzzaman M, Islam A, Supti SJ, Rifat MH, Islam MS, Ananna UH, Tusher KS, Al-Mutairi AA, Zaki MEA, Sarker S, Hosen ME (2025) J Comput Aided Mol Des 39(1):81\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSupti SJ, Hosen ME, Karikor MAS, Hasan MZ, Chowdhury MM, Hossain MM, Priota MF, Nuryay MN, Hasan BM, Islam MM (2025) ChemistrySelect 10(18):e202405525\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEssmann U, Perera L, Berkowitz ML, Darden T, Lee H, Pedersen LG (1995) J Chem Phys 103(19):8577\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQian W, Hurst C, Glass K, Harley D, Viennet E (2021) Trop Med Infect Disease 6(3):145\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePyke AT, Wilson DJ, Michie A, Mackenzie JS, Imrie A, Cameron J, Doggett SL, Haniotis J, Herrero LJ, Caly L, Lynch SE, Mee PT, Madzokere ET, Ramirez AL, Paramitha D, Hobson-Peters J, Smith DW, Weir R, Sullivan M, Druce J, Melville L, Robson J, Gibb R, van den Hurk AF, Duchene S (2024) Virus Evol 10(1)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichie A, Dhanasekaran V, Lindsay MDA, Neville PJ, Nicholson J, Jardine A, Mackenzie JS, Smith DW, Imrie A (2020) J Virol 94(2)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDamtew YT, Varghese BM, Anikeeva O, Tong M, Hansen A, Dear K, Zhang Y, Morgan G, Driscoll T, Capon T (2024) The Lancet Regional Health\u0026ndash;Western Pacific 48\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHime NJ, Wickens M, Doggett SL, Rahman K, Toi C, Webb C, Vyas A, Lachireddy K (2022) Australian and New Zealand. J Public Health 46(6):842\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoolhof IS, Beeton N, Bettiol S, Charleston M, Firestone SM, Gibney K, Neville P, Jardine A, Markey P, Kurucz N (2024) PLoS Pathog 20(2):e1011944\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi L, Jose J, Xiang Y, Kuhn RJ, Rossmann MG (2010) Nature 468(7324):705\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZimmerman O, Holmes AC, Kafai NM, Adams LJ, Diamond MS (2023) J Clin Investig 133(2)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eByrd EA, Kielian M (2017) mBio 8(6). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/mbio.01564\u003c/span\u003e\u003cspan address=\"10.1128/mbio.01564\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmaljohn AL, McClain D (1996) Medical Microbiology 4th edition\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJupille HJ, Medina-Rivera M, Hawman DW, Oko L, Morrison TE (2013) J Virol 87(10):5970\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFox JM, Huang L, Tahan S, Powell LA, Crowe JE Jr., Wang D, Diamond MS (2020) PLoS Pathog 16(8):e1008743\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGunn BM, Jones JE, Shabman RS, Whitmore AC, Sarkar S, Blevins LK, Morrison TE, Heise MT (2018) Virology 515:250\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFox JM, Long F, Edeling MA, Lin H, van Duijl-Richter MKS, Fong RH, Kahle KM, Smit JM, Jin J, Simmons G, Doranz BJ, Crowe JE Jr., Fremont DH, Rossmann MG, Diamond MS (2015) Cell 163(5):1095\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorres-Ruesta A, Chee RS, Ng LFP (2021) Microorganisms 9(5)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarplus M, McCammon JA (2002) Nat Struct Biol 9(9):646\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLemkul JA (2018) Living J Comput Mol Sci 1(1):5068\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLong F, Fong RH, Austin SK, Chen Z, Klose T, Fokine A, Liu Y, Porta J, Sapparapu G, Akahata W, Doranz BJ, Crowe JE Jr., Diamond MS, Rossmann MG (2015) Proc Natl Acad Sci U S A 112(45):13898\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnyder AJ, Sokoloski KJ, Mukhopadhyay S (2012) J Virol 86(6):3100\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePorta J, Jose J, Roehrig JT, Blair CD, Kuhn RJ, Rossmann MG (2014) J Virol 88(17):9616\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVoss JE, Vaney MC, Duquerroy S, Vonrhein C, Girard-Blanc C, Crublet E, Thompson A, Bricogne G, Rey FA (2010) Nature 468(7324):709\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLobanov MY, Bogatyreva N, Galzitskaya O (2008) Mol Biol 42(4):623\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArnittali M, Rissanou AN, Harmandaris V (2019) Procedia Comput Sci 156:69\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurin CD, Wilson IA, Ward AB (2019) Nat Microbiol 4(5):734\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKocer I, Celik E (2024) J Biomol Struct Dynamics 42(9):4699\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaruyama Y, Igarashi R, Ushiku Y, Mitsutake A (2023) J Chem Inf Model 63(5):1529\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Cn, Denzler LM, Hood OE, Martin AC (2024) Do antibody CDR loops change conformation upon binding? Taylor \u0026amp; Francis, MAbs, p 2322533\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerkhivker GM, Agajanian S, Kassab R, Krishnan K (2022) J Chem Inf Model 62(8):1956\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-computer-aided-molecular-design","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcam","sideBox":"Learn more about [Journal of Computer-Aided Molecular Design](http://link.springer.com/journal/10822)","snPcode":"10822","submissionUrl":"https://submission.nature.com/new-submission/10822/3","title":"Journal of Computer-Aided Molecular Design","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Ross River virus, E2 glycoprotein, spatiotemporal dynamics, mutant variants, antibody-antigen docking, molecular dynamics simulation","lastPublishedDoi":"10.21203/rs.3.rs-9535817/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9535817/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRoss River virus (RRV) is the most prevalent arthritogenic alphavirus in Australia, yet the structural consequences of envelope protein variation on antibody recognition remain poorly understood. Here, we analysed 85 complete RRV E2 sequences from Australia associated with documented infections and compared them with the earliest available wild type isolate to identify recurrent amino acid substitutions. Five top mutant variants (M1\u0026ndash;M5) were selected for detailed investigation. In silico predictions revealed that Y5H and R238K may destabilize local structure, while other substitutions variably modulate protein folding and surface exposure. Structural modelling and molecular docking with the broadly neutralizing RRV-12 Fab antibody indicated that M1 and M3 maintained interactions with epitopes similar to WT, whereas M2, M4, and M5 exhibited redistributed antibody contacts, with M4 showing the most pronounced shift, although overall binding energetics remained largely conserved. Molecular dynamics simulations revealed structural plasticity in surface-exposed loops, suggesting that these mutations may modulate antigenic presentation while preserving attachment and entry functions. These results reveal that naturally occurring E2 mutations can reshape antigenic surfaces without necessarily abolishing antibody recognition. Importantly, the conserved residue engaged by RRV-12 highlights a potential target for rational vaccine design aimed at eliciting broad, cross-protective immunity against circulating RRV variants.\u003c/p\u003e","manuscriptTitle":"Mutation-driven reorganisation of E2–RRV12 antibody binding interfaces across Ross River virus variants: A computational study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-28 13:23:17","doi":"10.21203/rs.3.rs-9535817/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"234514801913532932933845950710104829535","date":"2026-05-17T17:34:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T15:25:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47868843078707929916556982167008906265","date":"2026-05-10T14:25:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-10T12:44:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-07T21:20:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-28T07:53:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Computer-Aided Molecular Design","date":"2026-04-27T03:13:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-computer-aided-molecular-design","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcam","sideBox":"Learn more about [Journal of Computer-Aided Molecular Design](http://link.springer.com/journal/10822)","snPcode":"10822","submissionUrl":"https://submission.nature.com/new-submission/10822/3","title":"Journal of Computer-Aided Molecular Design","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3887050c-6d73-4eda-9610-d00d508358d7","owner":[],"postedDate":"April 28th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"234514801913532932933845950710104829535","date":"2026-05-17T17:34:13+00:00","index":13,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T15:25:39+00:00","index":11,"fulltext":""},{"type":"reviewerAgreed","content":"47868843078707929916556982167008906265","date":"2026-05-10T14:25:12+00:00","index":10,"fulltext":""},{"type":"reviewersInvited","content":"7","date":"2026-05-10T12:44:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-07T21:20:39+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-10T12:53:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-28 13:23:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9535817","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9535817","identity":"rs-9535817","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}