Lineage-Specific Adaptive Evolution of the Mosquito Fibrinogen-Related Protein FBN30 at a Predicted Parasite-Facing Interface

Lineage-Specific Adaptive Evolution of the Mosquito Fibrinogen-Related Protein FBN30 at a Predicted Parasite-Facing Interface | 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 Article Lineage-Specific Adaptive Evolution of the Mosquito Fibrinogen-Related Protein FBN30 at a Predicted Parasite-Facing Interface Krishnendu Sinha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8743126/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Fibrinogen-related proteins (FREPs) contribute to mosquito-parasite interactions, yet the evolutionary processes shaping their functional diversification remain poorly resolved. The mosquito protein FBN30 has been implicated in restricting Plasmodium development, but its molecular basis of action is unknown. Here, the study examines the evolutionary history of FBN30 across Anopheles mosquitoes to test whether lineage-specific adaptive evolution has modified its functional properties. Codon-based analyses of FBN30 orthologs from 29 Anopheles species reveal a single episode of strong episodic diversifying selection confined to the Anopheles darlingi lineage. Site-level tests identify a positively selected residue within the conserved fibrinogen-like (FBG) domain. Ancestral sequence reconstruction shows that this site underwent a serine-to-asparagine substitution along the A. darlingi lineage, with structural modeling indicating only modest local effects on protein stability. Using protein-protein docking and binding affinity prediction as a proxy for functional engagement, the study finds that the reconstructed ancestral FBN30 exhibits significantly stronger predicted affinity for Plasmodium falciparum α-tubulin-1 than the extant A. darlingi protein, whereas the derived substitution alone does not account for this difference. These results indicate that evolutionary divergence in FBN30 is associated with reduced predicted engagement at a parasite-facing interface and support a model in which inhibitory mosquito proteins undergo fine-scale adaptive refinement under parasite-mediated selective pressures. Biological sciences/Computational biology and bioinformatics Biological sciences/Evolution Anopheles darlingi Plasmodium falciparum FBN30 α-tubulin-1 ookinete Figures Figure 1 Figure 2 Figure 3 Figure 4 1. INTRODUCTION Malaria transmission depends on molecular interactions between Plasmodium parasites and their Anopheles mosquito vectors, particularly during ookinete traversal of the midgut epithelium [ 1 ]. This stage of the life cycle represents a critical evolutionary interface, where parasite success is shaped by compatibility with vector-derived immune and recognition factors. Among these, fibrinogen-related proteins (FREPs) constitute a diverse family of mosquito innate immune proteins implicated in modulating parasite development [ 2 – 8 ]. FREPs share a conserved fibrinogen-like (FBG) domain that mediates ligand recognition, yet individual family members differ markedly in their effects on parasite transmission. The best-characterized example, FREP1, facilitates Plasmodium invasion by interacting with α-tubulin-1 exposed on the ookinete surface [ 5 – 7 ]. In contrast, other FREPs appear to inhibit parasite development, suggesting functional diversification within the family. One such protein, FBN30, has been shown through functional genetic studies to restrict Plasmodium infection in Anopheles gambiae , as silencing of FBN30 results in increased parasite loads [ 9 ]. Despite this phenotype, the molecular and evolutionary basis of FBN30’s inhibitory role remains unresolved. FBN30 is a fibrinogen-related protein that adopts the conserved FREP/FBN structural architecture, characterized by a signal peptide for secretion and a C-terminal fibrinogen-like (FBG) domain of approximately 200 amino acids[ 10 ]. The FBG domain is predicted to form a β-sheet–rich fold with flexible surface-exposed loops that constitute the primary interaction interface for ligand binding, consistent with other invertebrate fibrinogen-related proteins[ 11 ]. In vivo, FBN30 assembles into a higher-order octameric complex, indicating quaternary structural organization beyond the monomeric FBG fold and suggesting cooperative or avidity-enhanced binding properties. While atomic-resolution structural data are not yet available, the strong conservation of the FBG core implies structural constraint, whereas naturally occurring substitutions, particularly within the signal peptide and putative surface regions, are expected to modulate protein abundance or interaction efficiency without disrupting overall fold integrity [ 10 , 11 ]. From an evolutionary perspective, FREPs represent compelling candidates for parasite-mediated selection. The FBG domain is structurally constrained yet features surface-exposed regions that may accommodate adaptive modification, enabling fine-scale tuning of recognition or binding properties without disrupting overall protein architecture. However, it remains unclear whether FBN30 has evolved primarily under purifying selection or whether specific mosquito lineages exhibit signatures of episodic adaptive evolution indicative of host–parasite antagonism. Here, the study investigates the evolutionary history of FBN30 across Anopheles mosquitoes using comparative genomics, codon-based models of molecular evolution, ancestral sequence reconstruction, and structural modeling. By integrating evolutionary inference with predicted functional consequences, the study tests whether lineage-specific adaptive evolution has shaped FBN30 at a putative parasite-facing interface. This approach provides an evolutionary framework for understanding how inhibitory mosquito proteins may be refined under parasite-mediated selective pressures, generating testable hypotheses about their role in mosquito– Plasmodium interactions. 2. MATERIALS AND METHODS 2.1. Retrieval of FBG30 sequence and ortholog identification The protein sequence of FBG30 from Anopheles gambiae PEST (VectorBase gene ID: AGAP006914) was retrieved from VectorBase[ 12 ]. These 280 amino-acid sequences served as the query for ortholog identification across Anopheles clade and the outgroup, Culex quinquefasciatus (VectorBase gene ID: CPIJ000937). Complete proteomes for 29 Anopheles and one Culex species available in VectorBase (release 68, accessed November 2025) were downloaded. Orthologs were identified using a reciprocal BLAST hit (RBH) workflow implemented through an in-house Python script implementing soft masking and Smith–Waterman alignments[ 13 ] with an E-value threshold of 1 × 10⁻⁵, and a minimum alignment coverage of 90%. Twenty-nine candidate sequences returning A. gambiae FBG30 as the top reciprocal match were accepted as true orthologs[ 14 ]. All sequences were further validated for the presence of the fibrinogen-related (FBG) domain using InterProScan[ 15 ], and each ortholog was confirmed to contain a canonical FBG region. Corresponding coding sequences (CDS) for all confirmed orthologs were retrieved from VectorBase using gene-level identifiers. 2.2. Multiple sequence alignment and trimming Protein sequences were aligned using PRANK v.170427 [ 16 ]with the codon-aware settings (default parameters) to preserve evolutionary signal and minimize gap misplacement. The resulting protein alignment was used to generate a codon-preserving nucleotide alignment via PAL2NAL v.14[ 17 ], producing an alignment of 1686 nucleotide positions. Both the protein and codon alignments were refined using ClipKIT v1.3 [ 18 ]with the kpic-smart-gap mode. For the codon alignment, ClipKIT produced a trimmed alignment of 747 positions, removing 55.69% of sites while preserving informative residues for evolutionary analysis. 2.3. Phylogenetic inference A maximum likelihood phylogeny of FBG30 protein orthologs was reconstructed using IQ-TREE3 v3.0.1[ 19 ]. ModelFinder [ 20 ] identified LG + I+G4 as the best-fitting amino acid substitution model under the Bayesian information criterion. Tree reconstruction included 1000 ultrafast bootstrap replicates and 1000 SH-aLRT tests [ 21 , 22 ]. The final ML tree contained 29 taxa and 274 amino-acid sites, with 241 parsimony-informative sites. The tree was used as the fixed topology for all downstream molecular evolutionary analyses [ 22 ]. 2.4. Detection of positive selection Episodic diversifying selection across branches was assessed using the adaptive Branch-Site Random Effects Likelihood (aBSREL) model [ 23 ] implemented in HyPhy v2.5[ 24 ]. The trimmed codon alignment and the ML tree were provided as input. Branch-specific likelihood ratio tests identified the A. darlingi FBG30 lineage (ADAR2_011252) as the only branch with significant evidence of episodic diversification (p = 0.0 after correction). To further investigate codon-specific selective pressures, a branch-site test in codeml (PAML v4.10.9) [ 25 , 26 ]was performed using the A. darlingi branch as the foreground. Bayes Empirical Bayes (BEB) analysis identified several sites with elevated posterior probability, including codon positions 51 (PP = 0.969) and 226 (PP = 0.842). Site-level episodic selection was tested using MEME [ 24 ]holding A. darlingi FBG30 as foreground, which detected codon 173 (CDS alignment) as significantly evolving under episodic selection (LRT = 4.63; p = 0.05). Mapping this site using in-house python script revealed that it corresponded to residue 218 in A. darlingi and residue 191 in the ancestral node. 2.5. Ancestral sequence reconstruction Ancestral sequence reconstruction (ASR) was performed using IQ-TREE v3.0.1 to infer the historical amino-acid states of FBG30 across the Anopheles phylogeny. The analysis used the same maximum-likelihood (ML) protein phylogeny that was previously inferred from the 29-sequence, 274-amino-acid alignment, along with the best-fit substitution model (LG + I+G4) selected by ModelFinder. IQ-TREE’s ASR procedure estimates, for every internal node and every alignment position, the most likely ancestral amino acid and its associated posterior probability, based on the fixed tree topology, branch lengths, and substitution model. The internal node representing the most recent common ancestor of A. darlingi and its sister taxon A. aquasalis was identified from the labeled ML tree and designated “Node 26” following the software’s node indexing. For each node, IQ-TREE provides reconstructed amino-acid sequences that can be exported as standard FASTA files using in house python script. To determine the ancestral state of the positively selected site, the codon identified by MEME (codon 173 in the CDS alignment) was mapped to its corresponding position in the ungapped protein sequence. After accounting for alignment gaps and restoring original residue numbering, this site corresponded to amino-acid position 218 in the A. darlingi FBG30 protein. Examination of the reconstructed Node 26 sequence showed that the corresponding position was occupied by a serine (S). In contrast, the extant A. darlingi sequence contains an asparagine (N) at the same position, indicating that the S→N substitution occurred along the A. darlingi lineage after divergence from A. aquasalis . The full ancestral sequence of Node 26 was used for all subsequent structural modeling, stability estimation, and protein–protein docking analyses, enabling direct comparison between the reconstructed ancestral state and the modern A. darlingi FBG30 protein. 2.6. Protein structure modeling Three-dimensional structures of the extant A. darlingi FBG30, the ancestral Node 26 variant, and the engineered N218S back-mutation were generated using the AlphaFold 3 prediction server[ 27 ]. For the N218S variant, the amino acid substitution was introduced manually using AliView [ 28 ] followed by de novo structure prediction. All predicted structures were used as starting models for docking and stability simulations. 2.7. Rosetta-based stability estimation Protein stability and the energetic effects of individual substitution were estimated using PyRosetta (Rosetta v2025)[ 29 ]. A standardized pipeline was applied to all variants using a Python script that performed 50 independent FastRelax replicates per sequence. Each replicate consisted of structure relaxation using the fa_scorefxn scoring function (full-atom score function) followed by calculation of Rosetta Energy Units (REU). ΔΔG values were computed as the difference between mutant and wild-type energies. This protocol produced stability profiles for the extant A. darlingi FBG30, the N218S mutant, and the Node 26 ancestor. 2.8. Protein–protein docking with α-tubulin-1 Protein–protein docking between FBG30 variants and Plasmodium falciparum α-tubulin-1 (UniProt Q6ZLZ9) was performed using HADDOCK 2.4[ 30 ]. Active residues within FBG30 were defined as those in the FBG domain (positions 92–302) based on InterProScan annotation.The experimentally mapped α-tubulin-1 linear epitope REDLAALEKD (residues 422–431) [ 31 ] as the core active site in HADDOCK docking, and expanded this region to residues 419–434 to allow for flanking contacts (passive residues 412–418 and 435–440 were auto-assigned/added). Passive residues were assigned automatically[ 30 ]. Docking was run using default parameters, and the resulting structures were clustered based on interface RMSD. Ten clusters were produced for each variant, each containing four water-refined models. Although the entire ensemble was used for binding energy assessment, the top-scoring HADDOCK cluster per variant was used for reporting docking statistics. 2.9. Binding affinity prediction Binding free energies (ΔG) for all docked complexes were estimated using PRODIGY v2.1 [ 32 , 33 ]installed locally through the Conda Bioconda distribution. For each variant, all 40 structures (10 clusters × 4 models) were processed independently using default temperature (25°C) through inhouse python script. The resulting distributions of predicted ΔG values were compared using two-tailed t-tests, after confirming normality with the Shapiro–Wilk test, implemented in an in-house R script. 2.10. Anopheles-Plasmodium coevolution through PACo and ParaFit Phylogenetic trees for 29 Anopheles species and 12 Plasmodium species were obtained in Newick format from published genomic resources and converted into patristic distance matrices using cophenetic() in the ape package (R v4.5.2). A natural host–parasite association matrix was constructed using only documented field infections and confirmed vector–parasite pairings, yielding a sparse 28 × 11 binary matrix in which taxa with no associations were removed to produce a final working matrix containing 20 Anopheles hosts, 5 Plasmodium parasites, and 5 confirmed natural links. Cophylogenetic congruence was evaluated using the Procrustean Approach to Cophylogeny (PACo) implemented in the paco package, with Cailliez correction applied to ensure Euclidean distance matrices and significance assessed using 10,000 permutations. To complement PACo, we applied ParaFit using the ade4 package, again using Cailliez-corrected host and parasite distance matrices and 9,999 permutations to obtain ParaFitGlobal and ParaFitLink statistics. Both analyses were performed on the trimmed natural matrix to ensure compatibility with the underlying algorithms. 3. Results 3.1. Identification of FBN30 orthologs and phylogenetic reconstruction Using the Anopheles gambiae FBN30 sequence as a reference, 29 one-to-one FBN30 orthologs across Anopheles species has been identified, with a single ortholog recovered from Culex quinquefasciatus as an outgroup. All sequences contained an intact fibrinogen-like (FBG) domain, confirming orthology and functional conservation. After alignment and trimming, the final dataset comprised 274 amino-acid positions and 747 codon sites. Maximum-likelihood phylogenetic inference recovered a topology broadly congruent with established Anopheles relationships, with strong branch support across most nodes (Fig. 1 ). The resulting tree was used as a fixed topology for all subsequent evolutionary analyses. 3.2. Lineage-specific episodic diversifying selection on FBN30 To assess whether FBN30 experienced adaptive evolution, branch- and site-based codon models has been applied. aBSREL detected significant episodic diversifying selection on a single branch corresponding to the Anopheles darlingi lineage, with no other branches showing evidence of ω > 1. Along this lineage, approximately 24% of sites were inferred to evolve under strong positive selection, indicating a localized episode of adaptive divergence rather than widespread relaxation of constraint (Table 1 ). Consistent with this result, branch-site analysis using codeml supported positive selection on the A. darlingi branch, yielding a significantly better fit than the null model (Table 1 ). Together, these analyses identify A. darlingi FBN30 as the sole lineage exhibiting detectable episodic adaptive evolution. Table 1 Summary of molecular evolution analyses identifying episodic and site-specific positive selection in Anopheles FBN30 orthologs. This table presents the results of aBSREL, the branch-site codeml model, and MEME analysis. The aBSREL test detected a single branch under episodic diversifying selection, corresponding to A. darlingi FBN30 (ADAR2_011252_R18153). The branch-site model similarly supported positive selection on this lineage, with one codon site (*) showing elevated posterior probabilities (BEB ≥ 0.95). MEME analysis identified codon 173 (corresponding to residue 218 in A. darlingi and residue 191 in Node 26) as evolving under episodic positive selection with a significant LRT. The combined results support the presence of a lineage-specific adaptive substitution along the A. darlingi branch. Analysis Key Result Statistical Support Notes aBSREL A. darlingi branch under episodic diversifying selection p = 0.0; ω₂ = 57.25 at 23.6% sites Strong evidence for branch-specific selection Branch-site codeml Several sites under selection in A. darlingi foreground LRT = 6.72; p = 0.01 BEB ≥ 0.969* at codon 51; BEB ≥ 0.842 at codon 226; moderate support at others Confirms aBSREL signal MEME Codon 173 under episodic selection LRT = 4.63; p = 0.05 Selected residue maps to FBN30 FBG domain 3.3. Site-level selection and localization within the FBG domain Site-level tests using MEME identified a single codon evolving under episodic positive selection specifically along the A. darlingi lineage (Table 1 ). This codon maps to a residue within the conserved FBG domain, a region implicated in ligand recognition across fibrinogen-related proteins (Fig. 4 ). Although codeml and MEME differed in the specific sites receiving highest posterior support-reflecting their distinct statistical sensitivities, both approaches converged on the conclusion that adaptive evolution in FBN30 is limited in scope and concentrated within a functionally relevant domain. 3.4. Ancestral state reconstruction reveals derived substitution in A. darlingi Ancestral sequence reconstruction was performed to place the positively selected site in an evolutionary context. The reconstructed ancestor shared by A. darlingi and its sister taxon A. aquasalis carried a serine at the selected position (Fig. 1 ), whereas the extant A. darlingi sequence contains an asparagine (Table 1 , 2 ). This serine-to-asparagine substitution therefore represents a derived change unique to the A. darlingi lineage and was used as a focal point for downstream structural analyses. Table 2 Rosetta ΔΔG stability results for extant A. darlingi FBN30, the N218S mutant, and the reconstructed Node 26 ancestor. Rosetta FastRelax was performed with ten independent replicates to estimate the energetic consequences of the selected substitution. The extant A. darlingi FBN30 exhibited more favorable energies than the Node 26 ancestor in all replicates, whereas the N218S mutation introduced into the extant protein produced moderate and variable destabilization. Variant Mean ΔΔG (REU) ± SD Interpretation Notes N218S (relative to WT) 3.93 ± 7.04 Mild destabilization Highly variable between replicates Node 26 ancestor (relative to WT) 237.62 ± 14.68 Strong destabilization Indicates globally less stable ancestral fold 3.5. Structural stability reflects fine-scale evolutionary modulation To evaluate whether the derived substitution produced major structural effects, predicted protein stability across extant, ancestral, and back-mutated FBN30 variants was compared (Table 2 , Fig. 2 ). The reconstructed ancestral protein exhibited substantially reduced predicted stability relative to the extant A. darlingi protein, reflecting cumulative divergence across the lineage rather than the effect of any single substitution. In contrast, introducing the ancestral residue into the extant background produced only modest and variable stability changes, indicating that the positively selected site contributes to localized modulation rather than global fold alteration. 3.6. Evolutionary divergence is associated with altered predicted ligand engagement To explore potential functional consequences of FBN30 evolution, the study used protein-protein docking and binding affinity prediction as a proxy for ligand engagement (Fig. 4 ). Comparative analyses revealed that the reconstructed ancestral FBN30 consistently exhibited stronger predicted affinity for Plasmodium falciparum α-tubulin-1 than the extant A. darlingi protein (Fig. 3 ). This difference was robust across docking metrics and binding energy estimates (Tables 3 & 4 ). Importantly, the derived substitution alone did not account for the full magnitude of the observed affinity reduction, suggesting that evolutionary divergence along the A. darlingi lineage involved cumulative changes affecting a parasite-facing interface. Table 3 HADDOCK docking statistics for FBN30 variants interacting with Plasmodium falciparum α-tubulin-1. This table summarizes the best-scoring docked clusters for each FBN30 variant. The Node 26 ancestral sequence exhibited the most favorable docking energetics, lower RMSD, and greater buried surface area compared to the WT and N218S forms. These patterns are consistent with stronger and more stable binding in the ancestral FBN30-tubulin complex. Variant Best Cluster HADDOCK Score Cluster Size RMSD (Å) Buried Surface Area (Ų) Z-score WT Cluster 8 125.7 ± 22.8 5 1.3 ± 1.4 2202.0 ± 98.0 –1.7 Node 26 ancestor Cluster 4 62.5 ± 10.6 22 3.2 ± 0.1 2834.4 ± 38.4 –2.2 N218S mutant Cluster 1 127.6 ± 4.0 23 22.3 ± 0.4 2194.8 ± 29.6 –1.9 Table 4 PRODIGY binding free energy predictions (ΔG) for the interaction between FBN30 variants and P. falciparum α-tubulin-1. Binding energies were computed for all clusters (40 structures per variant). Node 26 exhibited significantly stronger binding than WT, whereas N218S did not differ significantly from WT. Comparison Mean ΔG (kcal·mol⁻¹) Difference Statistical Test p-value Interpretation WT vs Node 26 –12.16 vs − 13.87 + 1.71 kcal·mol⁻¹ Mann-Whitney U 4.52 × 10⁻¹⁰ Node 26 binds significantly more strongly WT vs N218S –12.16 vs − 12.50 + 0.34 kcal·mol⁻¹ Mann-Whitney U ns No significant effect 3.7. Global but not pairwise host-parasite phylogenetic congruence Cophylogenetic analyses using PACo and ParaFit revealed significant global congruence between Anopheles and Plasmodium phylogenies based on documented natural associations. However, sparse host–parasite linkages precluded reliable identification of individual coevolving pairs. These results indicate broad phylogenetic structuring of vector-parasite associations rather than strict pairwise co-speciation, consistent with lineage-specific rather than system-wide antagonistic evolution. 4. Discussion FREPs are central components of mosquito innate immunity, yet their evolutionary dynamics and functional diversification remain incompletely understood. By integrating codon-based evolutionary analyses, ancestral sequence reconstruction, and structural modeling, this study identifies a discrete episode of episodic diversifying selection acting on FBN30 in the A. darlingi lineage (Table 1 ; Fig. 1 ). This adaptive signal is restricted to a single mosquito lineage and absent from other Anopheles species, indicating localized evolutionary refinement rather than widespread diversification across the FREP family. Both branch-level and site-level analyses converge on this conclusion, with the positively selected residue mapping to the conserved FBG domain (Fig. 4 ), a region implicated in ligand recognition in fibrinogen-related proteins. The confinement of adaptive evolution to a single lineage suggests that FBN30 has been shaped by lineage-specific selective pressures, potentially reflecting geographically or ecologically structured host–parasite interactions. Ancestral sequence reconstruction places the derived substitution in a clear evolutionary context, revealing a serine-to-asparagine change unique to A. darlingi . Structural stability analyses indicate that this substitution produces only modest and localized effects, whereas the reconstructed ancestral protein differs substantially in overall predicted stability (Table 2 ; Fig. 2 ). This pattern suggests that the adaptive substitution represents fine-scale functional tuning occurring on a background of broader evolutionary divergence, rather than a single mutation driving major structural reorganization. Such incremental refinement is consistent with expectations for proteins subject to ongoing functional constraint, where adaptive change is accommodated through subtle modulation rather than wholesale innovation. To explore potential functional consequences of this evolutionary divergence, protein-protein docking and binding affinity prediction as proxies for ligand engagement has been employed. These analyses consistently indicate that the reconstructed ancestral FBN30 exhibits stronger predicted affinity for Plasmodium falciparum α-tubulin-1 than the extant A. darlingi protein (Tables 3 & 4 ; Fig. 3 ). Although docking-based predictions cannot establish biochemical interaction, the concordance between evolutionary signal and predicted functional modulation suggests that adaptive evolution in FBN30 may have altered a parasite-facing interface. Notably, the derived substitution alone does not account for the full reduction in predicted affinity, implying that cumulative evolutionary changes along the A. darlingi lineage contribute to this effect. Taken together, these findings are consistent with a model in which inhibitory mosquito proteins undergo lineage-specific adaptive refinement under parasite-mediated selective pressures. Rather than indicating the emergence or loss of function, the observed evolutionary pattern suggests modulation of an existing recognition interface, potentially altering the strength or outcome of parasite engagement. Such a scenario aligns with theoretical expectations of antagonistic host-parasite evolution, where reciprocal pressures favor incremental adjustments rather than dramatic functional shifts. Cophylogenetic analyses reveal significant global congruence between Anopheles and Plasmodium phylogenies, supporting the view that vector-parasite associations are shaped by broad evolutionary constraints. However, the absence of detectable pairwise co-speciation underscores that adaptive events such as those observed in A. darlingi FBN30 likely represent localized evolutionary responses rather than system-wide reciprocal evolution. This distinction highlights the importance of integrating lineage-specific molecular analyses with broader phylogenetic context when interpreting host–parasite evolution. Several limitations of this study warrant consideration. All functional inferences are based on in silico predictions and should be interpreted as hypotheses rather than demonstrations of molecular interaction. Moreover, the focus on a single mosquito lineage limits generalization across Anopheles . Nevertheless, the convergence of evolutionary, structural, and predictive functional analyses provides a coherent framework for understanding how inhibitory mosquito proteins may be shaped by parasite-mediated selection. 5. Conclusion This study identifies lineage-specific adaptive evolution in FBN30 and suggests that fine-scale evolutionary refinement of a conserved recognition domain may modulate predicted parasite engagement. By emphasizing evolutionary inference over mechanistic assertion, this work contributes to a growing understanding of how host innate immune factors diversify under antagonistic interactions and generates testable hypotheses for future experimental investigation. Declarations Funding details This research received no funding. Conflicts of interest The author report that there are no competing interests to declare. Data availability statement All data associated with this study are available on Figshare (https://doi.org/10.6084/m9.figshare.30618974). The materials provided include both raw and processed datasets, as well as all scripts required to reproduce the analyses described in the manuscript. Supplementary files contained within the repository and the manuscript ensure full transparency and reproducibility of the work. Acknowledgments I thank the global open-access community for their commitment to freely accessible science, without which this study would not have been possible. I gratefully acknowledge VectorBase for providing high-quality genomic resources essential to this work. I also recognize the contributions of openly available language models that assisted in refining the manuscript. Finally, I am deeply grateful to my wife, Dr. Nabanita Ghosh, Assistant Professor of Zoology at Maulana Azad College, Kolkata, whose insightful discussions helped shape the central idea of this study. References Daily, J. P. & Parikh, S. Malaria. New England Journal of Medicine 392 , 1320–1333 (2025). Shaw, W. R., Marcenac, P. & Catteruccia, F. Plasmodium development in Anopheles: a tale of shared resources. Trends Parasitol. 38 , 124 (2021). Zhang, G. et al. Anopheles Midgut FREP1 Mediates Plasmodium Invasion. J. Biol. Chem. 290 , 16490 (2015). Niu, G. et al. The fibrinogen-like domain of FREP1 protein is a broad-spectrum malaria transmission-blocking vaccine antigen. Journal of Biological Chemistry 292 , 11960–11969 (2017). Zhang, G. et al. Targeting plasmodium α-tubulin-1 to block malaria transmission to mosquitoes. Front. Cell. Infect. Microbiol. 13 , 1132647 (2023). Zhang, G., Niu, G., Perez, L., Wang, X. & Li, J. Malaria transmission assisted by interaction between Plasmodium α-tubulin-1 and Anopheles FREP1 protein. bioRxiv 2019.12.16.878082 (2019) doi:10.1101/2019.12.16.878082. Hanington, P. C. & Zhang, S. M. The Primary Role of Fibrinogen-Related Proteins in Invertebrates Is Defense, Not Coagulation. J. Innate Immun. 3 , 17 (2010). Molina-Cruz, A. et al. Some strains of Plasmodium falciparum, a human malaria parasite, evade the complement-like system of Anopheles gambiae mosquitoes. Proc. Natl. Acad. Sci. U. S. A. 109 , (2012). Li, J. et al. Genome-block expression-assisted association studies discover malaria resistance genes in Anopheles gambiae. Proceedings of the National Academy of Sciences 110 , 20675–20680 (2013). Niu, G. et al. FBN30 in wild Anophelesgambiae functions as a pathogen recognition molecule against clinically circulating Plasmodiumfalciparum in malaria endemic areas in Kenya. Sci. Rep. 7 , 8577 (2017). Janjoter, S. et al. Deciphering the molecular targets of Plasmodium and Anopheline interactions for malaria control. FEBS Journal https://doi.org/10.1111/FEBS.70250;WEBSITE:WEBSITE:FEBS;PAGE:STRING:ARTICLE/CHAPTER (2025) doi:10.1111/FEBS.70250;WEBSITE:WEBSITE:FEBS;PAGE:STRING:ARTICLE/CHAPTER. Giraldo-Calderón, G. I. et al. VectorBase: an updated bioinformatics resource for invertebrate vectors and other organisms related with human diseases. Nucleic Acids Res. 43 , D707 (2014). Moreno-Hagelsieb, G. & Latimer, K. Choosing BLAST options for better detection of orthologs as reciprocal best hits. Bioinformatics 24 , 319–324 (2008). Hernández-Salmerón, J. E. & Moreno-Hagelsieb, G. Progress in quickly finding orthologs as reciprocal best hits: comparing blast, last, diamond and MMseqs2. BMC Genomics 2020 21:1 21 , 741- (2020). Jones, P. et al. InterProScan 5: genome-scale protein function classification. Bioinformatics 30 , 1236–1240 (2014). Löytynoja, A. Phylogeny-Aware Alignment with PRANK and PAGAN. Methods Mol. Biol. 2231 , 17–37 (2021). Suyama, M., Torrents, D. & Bork, P. PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic Acids Res. 34 , W609 (2006). Steenwyk, J. L., Buida, T. J., Li, Y., Shen, X. X. & Rokas, A. ClipKIT: A multiple sequence alignment trimming software for accurate phylogenomic inference. PLoS Biol. 18 , e3001007 (2020). Wong, T. K. F. et al. IQ-TREE 3: Phylogenomic Inference Software using Complex Evolutionary Models. https://doi.org/10.32942/X2P62N (2025) doi:10.32942/X2P62N. Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., Von Haeseler, A. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nature Methods 2017 14:6 14 , 587–589 (2017). Sinha, K. Episodic Positive Selection Signatures in Arabidopsis CONSTANS-like Genes COL3 and COL5 Indicating Adaptive Evolution in Red-Light Signaling Pathways. bioRxiv 2025.04.03.646976 (2025) doi:10.1101/2025.04.03.646976. Sinha, K., Jana, S., Pramanik, P. & Bera, B. Selection on synonymous codon usage in soybean (Glycine max) WRKY genes. Scientific Reports 2024 14:1 14 , 26530- (2024). Smith, M. D. et al. Less Is More: An Adaptive Branch-Site Random Effects Model for Efficient Detection of Episodic Diversifying Selection. Mol. Biol. Evol. 32 , 1342 (2015). Kosakovsky Pond, S. L. et al. HyPhy 2.5—A Customizable Platform for Evolutionary Hypothesis Testing Using Phylogenies. Mol. Biol. Evol. 37 , 295 (2019). Yang, Z. PAML 4: Phylogenetic Analysis by Maximum Likelihood. Mol. Biol. Evol. 24 , 1586–1591 (2007). Álvarez-Carretero, S., Kapli, P. & Yang, Z. Beginner’s Guide on the Use of PAML to Detect Positive Selection. Mol. Biol. Evol. 40 , (2023). Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021 596:7873 596 , 583–589 (2021). Larsson, A. AliView: a fast and lightweight alignment viewer and editor for large datasets. Bioinformatics 30 , 3276–3278 (2014). Sora, V. et al. RosettaDDGPrediction for high‐throughput mutational scans: From stability to binding. Protein Sci. 32 , e4527 (2023). Honorato, R. V. et al. The HADDOCK2.4 web server for integrative modeling of biomolecular complexes. Nature Protocols 2024 19:11 19 , 3219–3241 (2024). Zhang, G. et al. Targeting plasmodium α-tubulin-1 to block malaria transmission to mosquitoes. Front. Cell. Infect. Microbiol. 13 , 1132647 (2023). Xue, L. C., Rodrigues, J. P., Kastritis, P. L., Bonvin, A. M. & Vangone, A. PRODIGY: a web server for predicting the binding affinity of protein–protein complexes. Bioinformatics 32 , 3676–3678 (2016). Vangone, A. & Bonvin, A. M. J. J. Contacts-based prediction of binding affinity in protein–protein complexes. Elife 4 , (2015). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 16 May, 2026 Reviewers agreed at journal 11 May, 2026 Reviews received at journal 01 Apr, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers invited by journal 07 Feb, 2026 Editor assigned by journal 07 Feb, 2026 Editor invited by journal 03 Feb, 2026 Submission checks completed at journal 02 Feb, 2026 First submitted to journal 02 Feb, 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-8743126","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":590481398,"identity":"02d769b7-6c2b-477f-a4f5-904c4548d3bb","order_by":0,"name":"Krishnendu Sinha","email":"data:image/png;base64,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","orcid":"","institution":"Jhargram Raj College","correspondingAuthor":true,"prefix":"","firstName":"Krishnendu","middleName":"","lastName":"Sinha","suffix":""}],"badges":[],"createdAt":"2026-01-30 16:10:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8743126/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8743126/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102564282,"identity":"449c35cf-85af-4094-9f8b-d6afbbc96535","added_by":"auto","created_at":"2026-02-13 05:20:10","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":948657,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMaximum likelihood phylogeny of FBN30 across 29 \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAnopheles\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e species. \u003c/strong\u003eThe phylogeny was inferred using IQ-TREE3 under the LG+I+G4 substitution model, with 1000 ultrafast bootstrap replicates and 1000 SH-aLRT tests. Branch lengths represent amino-acid substitutions per site. The tree is rooted using the \u003cem\u003eCulex quinquefasciatus\u003c/em\u003e FBN30 ortholog (VectorBase gene ID: CPIJ000937), which served as the designated outgroup. The A. darlingi lineage (ADAR2_011252_R18153) is highlighted as the only branch exhibiting significant evidence of episodic diversifying selection in the aBSREL analysis.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8743126/v1/9a1bd627bb45790ba3221eeb.jpeg"},{"id":102747086,"identity":"fe6abb83-7420-458e-b6ed-3d793688cd2b","added_by":"auto","created_at":"2026-02-16 09:03:47","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":145126,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRosetta stability estimates for WT, N218S, and Node 26 FBN30.\u003c/strong\u003e Replicate ΔΔG values are shown for each comparison. The Node 26 ancestor exhibits uniformly large positive ΔΔG values relative to WT, consistent with global destabilization (left panel), whereas the N218S mutation shows modest and variable effects (right panel).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8743126/v1/59c1eede4733f623761685c2.jpeg"},{"id":102564284,"identity":"c54826ad-50ab-4971-908f-7d40ccf25b6c","added_by":"auto","created_at":"2026-02-13 05:20:10","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":71903,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of PRODIGY-predicted binding energies for WT, N218S, and Node 26 FBN30.\u003c/strong\u003e Boxplots display ΔG (kcal·mol⁻¹) across 40 docked structures per variant. The Node 26 distribution is significantly shifted toward more negative binding energies, whereas the N218S distribution overlaps the WT (not shown).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8743126/v1/c3a0e9363cb9bf6c4ef61c37.jpeg"},{"id":102564285,"identity":"8d1ca7b3-0453-47ab-b868-0c1cd3813742","added_by":"auto","created_at":"2026-02-13 05:20:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":625904,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative structural analysis of WT, N218S, and Node 26 FBN30 variants docked to \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eP. falciparum\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e α-tubulin-1. \u003c/strong\u003eHADDOCK best-scoring complexes are shown for the three FREP1 variants: WT (A), N218S mutant (B), and Node 26 ancestral variant (C). For each variant, the overall docked assembly is displayed in ribbon representations, followed by a space-filling dimer view highlighting the variant-specific residue (WT: 218; N218S: Ser218; Node 26: position 191) in distinct color. Corresponding close-up views of the binding interface are shown as electrostatic surfaces and hydrophobicity-mapped surfaces, illustrating differences in charge complementarity, nonpolar contacts, and interface geometry among the three variants. Collectively, these comparisons highlight the expanded buried surface area and altered interaction landscape of the Node 26 complex relative to WT and N218S.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8743126/v1/f8eebe65b2b5234de7cc8d70.png"},{"id":102750654,"identity":"6eab537e-0f0d-48d8-9e19-58488630386a","added_by":"auto","created_at":"2026-02-16 09:21:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3100029,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8743126/v1/db4ea6d7-0d71-430d-a697-d72a3409847e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Lineage-Specific Adaptive Evolution of the Mosquito Fibrinogen-Related Protein FBN30 at a Predicted Parasite-Facing Interface","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eMalaria transmission depends on molecular interactions between \u003cem\u003ePlasmodium\u003c/em\u003e parasites and their \u003cem\u003eAnopheles\u003c/em\u003e mosquito vectors, particularly during ookinete traversal of the midgut epithelium [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This stage of the life cycle represents a critical evolutionary interface, where parasite success is shaped by compatibility with vector-derived immune and recognition factors. Among these, fibrinogen-related proteins (FREPs) constitute a diverse family of mosquito innate immune proteins implicated in modulating parasite development [\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFREPs share a conserved fibrinogen-like (FBG) domain that mediates ligand recognition, yet individual family members differ markedly in their effects on parasite transmission. The best-characterized example, FREP1, facilitates \u003cem\u003ePlasmodium\u003c/em\u003e invasion by interacting with α-tubulin-1 exposed on the ookinete surface [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In contrast, other FREPs appear to inhibit parasite development, suggesting functional diversification within the family. One such protein, FBN30, has been shown through functional genetic studies to restrict \u003cem\u003ePlasmodium\u003c/em\u003e infection in \u003cem\u003eAnopheles gambiae\u003c/em\u003e, as silencing of FBN30 results in increased parasite loads [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Despite this phenotype, the molecular and evolutionary basis of FBN30\u0026rsquo;s inhibitory role remains unresolved.\u003c/p\u003e \u003cp\u003eFBN30 is a fibrinogen-related protein that adopts the conserved FREP/FBN structural architecture, characterized by a signal peptide for secretion and a C-terminal fibrinogen-like (FBG) domain of approximately 200 amino acids[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The FBG domain is predicted to form a β-sheet\u0026ndash;rich fold with flexible surface-exposed loops that constitute the primary interaction interface for ligand binding, consistent with other invertebrate fibrinogen-related proteins[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In vivo, FBN30 assembles into a higher-order octameric complex, indicating quaternary structural organization beyond the monomeric FBG fold and suggesting cooperative or avidity-enhanced binding properties. While atomic-resolution structural data are not yet available, the strong conservation of the FBG core implies structural constraint, whereas naturally occurring substitutions, particularly within the signal peptide and putative surface regions, are expected to modulate protein abundance or interaction efficiency without disrupting overall fold integrity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom an evolutionary perspective, FREPs represent compelling candidates for parasite-mediated selection. The FBG domain is structurally constrained yet features surface-exposed regions that may accommodate adaptive modification, enabling fine-scale tuning of recognition or binding properties without disrupting overall protein architecture. However, it remains unclear whether FBN30 has evolved primarily under purifying selection or whether specific mosquito lineages exhibit signatures of episodic adaptive evolution indicative of host\u0026ndash;parasite antagonism.\u003c/p\u003e \u003cp\u003eHere, the study investigates the evolutionary history of FBN30 across \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes using comparative genomics, codon-based models of molecular evolution, ancestral sequence reconstruction, and structural modeling. By integrating evolutionary inference with predicted functional consequences, the study tests whether lineage-specific adaptive evolution has shaped FBN30 at a putative parasite-facing interface. This approach provides an evolutionary framework for understanding how inhibitory mosquito proteins may be refined under parasite-mediated selective pressures, generating testable hypotheses about their role in mosquito\u0026ndash;\u003cem\u003ePlasmodium\u003c/em\u003e interactions.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Retrieval of FBG30 sequence and ortholog identification\u003c/h2\u003e \u003cp\u003eThe protein sequence of FBG30 from \u003cem\u003eAnopheles gambiae\u003c/em\u003e PEST (VectorBase gene ID: AGAP006914) was retrieved from VectorBase[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These 280 amino-acid sequences served as the query for ortholog identification across \u003cem\u003eAnopheles\u003c/em\u003e clade and the outgroup, \u003cem\u003eCulex quinquefasciatus\u003c/em\u003e (VectorBase gene ID: CPIJ000937). Complete proteomes for 29 \u003cem\u003eAnopheles\u003c/em\u003e and one \u003cem\u003eCulex\u003c/em\u003e species available in VectorBase (release 68, accessed November 2025) were downloaded. Orthologs were identified using a reciprocal BLAST hit (RBH) workflow implemented through an in-house Python script implementing soft masking and Smith\u0026ndash;Waterman alignments[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] with an E-value threshold of 1 \u0026times; 10⁻⁵, and a minimum alignment coverage of 90%. Twenty-nine candidate sequences returning \u003cem\u003eA. gambiae\u003c/em\u003e FBG30 as the top reciprocal match were accepted as true orthologs[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. All sequences were further validated for the presence of the fibrinogen-related (FBG) domain using InterProScan[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and each ortholog was confirmed to contain a canonical FBG region. Corresponding coding sequences (CDS) for all confirmed orthologs were retrieved from VectorBase using gene-level identifiers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Multiple sequence alignment and trimming\u003c/h2\u003e \u003cp\u003eProtein sequences were aligned using PRANK v.170427 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]with the codon-aware settings (default parameters) to preserve evolutionary signal and minimize gap misplacement. The resulting protein alignment was used to generate a codon-preserving nucleotide alignment via PAL2NAL v.14[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], producing an alignment of 1686 nucleotide positions. Both the protein and codon alignments were refined using ClipKIT v1.3 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]with the kpic-smart-gap mode. For the codon alignment, ClipKIT produced a trimmed alignment of 747 positions, removing 55.69% of sites while preserving informative residues for evolutionary analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Phylogenetic inference\u003c/h2\u003e \u003cp\u003eA maximum likelihood phylogeny of FBG30 protein orthologs was reconstructed using IQ-TREE3 v3.0.1[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. ModelFinder [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] identified LG\u0026thinsp;+\u0026thinsp;I+G4 as the best-fitting amino acid substitution model under the Bayesian information criterion. Tree reconstruction included 1000 ultrafast bootstrap replicates and 1000 SH-aLRT tests [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The final ML tree contained 29 taxa and 274 amino-acid sites, with 241 parsimony-informative sites. The tree was used as the fixed topology for all downstream molecular evolutionary analyses [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Detection of positive selection\u003c/h2\u003e \u003cp\u003eEpisodic diversifying selection across branches was assessed using the adaptive Branch-Site Random Effects Likelihood (aBSREL) model [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] implemented in HyPhy v2.5[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The trimmed codon alignment and the ML tree were provided as input. Branch-specific likelihood ratio tests identified the \u003cem\u003eA. darlingi\u003c/em\u003e FBG30 lineage (ADAR2_011252) as the only branch with significant evidence of episodic diversification (p\u0026thinsp;=\u0026thinsp;0.0 after correction). To further investigate codon-specific selective pressures, a branch-site test in codeml (PAML v4.10.9) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]was performed using the \u003cem\u003eA. darlingi\u003c/em\u003e branch as the foreground. Bayes Empirical Bayes (BEB) analysis identified several sites with elevated posterior probability, including codon positions 51 (PP\u0026thinsp;=\u0026thinsp;0.969) and 226 (PP\u0026thinsp;=\u0026thinsp;0.842). Site-level episodic selection was tested using MEME [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]holding \u003cem\u003eA. darlingi\u003c/em\u003e FBG30 as foreground, which detected codon 173 (CDS alignment) as significantly evolving under episodic selection (LRT\u0026thinsp;=\u0026thinsp;4.63; p\u0026thinsp;=\u0026thinsp;0.05). Mapping this site using in-house python script revealed that it corresponded to residue 218 in \u003cem\u003eA. darlingi\u003c/em\u003e and residue 191 in the ancestral node.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Ancestral sequence reconstruction\u003c/h2\u003e \u003cp\u003eAncestral sequence reconstruction (ASR) was performed using IQ-TREE v3.0.1 to infer the historical amino-acid states of FBG30 across the Anopheles phylogeny. The analysis used the same maximum-likelihood (ML) protein phylogeny that was previously inferred from the 29-sequence, 274-amino-acid alignment, along with the best-fit substitution model (LG\u0026thinsp;+\u0026thinsp;I+G4) selected by ModelFinder. IQ-TREE\u0026rsquo;s ASR procedure estimates, for every internal node and every alignment position, the most likely ancestral amino acid and its associated posterior probability, based on the fixed tree topology, branch lengths, and substitution model. The internal node representing the most recent common ancestor of \u003cem\u003eA. darlingi\u003c/em\u003e and its sister taxon \u003cem\u003eA. aquasalis\u003c/em\u003e was identified from the labeled ML tree and designated \u0026ldquo;Node 26\u0026rdquo; following the software\u0026rsquo;s node indexing. For each node, IQ-TREE provides reconstructed amino-acid sequences that can be exported as standard FASTA files using in house python script.\u003c/p\u003e \u003cp\u003eTo determine the ancestral state of the positively selected site, the codon identified by MEME (codon 173 in the CDS alignment) was mapped to its corresponding position in the ungapped protein sequence. After accounting for alignment gaps and restoring original residue numbering, this site corresponded to amino-acid position 218 in the \u003cem\u003eA. darlingi\u003c/em\u003e FBG30 protein. Examination of the reconstructed Node 26 sequence showed that the corresponding position was occupied by a serine (S). In contrast, the extant \u003cem\u003eA. darlingi\u003c/em\u003e sequence contains an asparagine (N) at the same position, indicating that the S\u0026rarr;N substitution occurred along the \u003cem\u003eA. darlingi\u003c/em\u003e lineage after divergence from \u003cem\u003eA. aquasalis\u003c/em\u003e. The full ancestral sequence of Node 26 was used for all subsequent structural modeling, stability estimation, and protein\u0026ndash;protein docking analyses, enabling direct comparison between the reconstructed ancestral state and the modern \u003cem\u003eA. darlingi\u003c/em\u003e FBG30 protein.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Protein structure modeling\u003c/h2\u003e \u003cp\u003eThree-dimensional structures of the extant \u003cem\u003eA. darlingi\u003c/em\u003e FBG30, the ancestral Node 26 variant, and the engineered N218S back-mutation were generated using the AlphaFold 3 prediction server[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. For the N218S variant, the amino acid substitution was introduced manually using AliView [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] followed by de novo structure prediction. All predicted structures were used as starting models for docking and stability simulations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Rosetta-based stability estimation\u003c/h2\u003e \u003cp\u003eProtein stability and the energetic effects of individual substitution were estimated using PyRosetta (Rosetta v2025)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A standardized pipeline was applied to all variants using a Python script that performed 50 independent FastRelax replicates per sequence. Each replicate consisted of structure relaxation using the fa_scorefxn scoring function (full-atom score function) followed by calculation of Rosetta Energy Units (REU). ΔΔG values were computed as the difference between mutant and wild-type energies. This protocol produced stability profiles for the extant \u003cem\u003eA. darlingi\u003c/em\u003e FBG30, the N218S mutant, and the Node 26 ancestor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Protein\u0026ndash;protein docking with α-tubulin-1\u003c/h2\u003e \u003cp\u003eProtein\u0026ndash;protein docking between FBG30 variants and \u003cem\u003ePlasmodium falciparum\u003c/em\u003e α-tubulin-1 (UniProt Q6ZLZ9) was performed using HADDOCK 2.4[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Active residues within FBG30 were defined as those in the FBG domain (positions 92\u0026ndash;302) based on InterProScan annotation.The experimentally mapped α-tubulin-1 linear epitope REDLAALEKD (residues 422\u0026ndash;431) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] as the core active site in HADDOCK docking, and expanded this region to residues 419\u0026ndash;434 to allow for flanking contacts (passive residues 412\u0026ndash;418 and 435\u0026ndash;440 were auto-assigned/added). Passive residues were assigned automatically[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Docking was run using default parameters, and the resulting structures were clustered based on interface RMSD. Ten clusters were produced for each variant, each containing four water-refined models. Although the entire ensemble was used for binding energy assessment, the top-scoring HADDOCK cluster per variant was used for reporting docking statistics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Binding affinity prediction\u003c/h2\u003e \u003cp\u003eBinding free energies (ΔG) for all docked complexes were estimated using PRODIGY v2.1 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]installed locally through the Conda Bioconda distribution. For each variant, all 40 structures (10 clusters \u0026times; 4 models) were processed independently using default temperature (25\u0026deg;C) through inhouse python script. The resulting distributions of predicted ΔG values were compared using two-tailed t-tests, after confirming normality with the Shapiro\u0026ndash;Wilk test, implemented in an in-house R script.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10. Anopheles-Plasmodium coevolution through PACo and ParaFit\u003c/h2\u003e \u003cp\u003ePhylogenetic trees for 29 Anopheles species and 12 Plasmodium species were obtained in Newick format from published genomic resources and converted into patristic distance matrices using cophenetic() in the ape package (R v4.5.2). A natural host\u0026ndash;parasite association matrix was constructed using only documented field infections and confirmed vector\u0026ndash;parasite pairings, yielding a sparse 28 \u0026times; 11 binary matrix in which taxa with no associations were removed to produce a final working matrix containing 20 Anopheles hosts, 5 Plasmodium parasites, and 5 confirmed natural links. Cophylogenetic congruence was evaluated using the Procrustean Approach to Cophylogeny (PACo) implemented in the paco package, with Cailliez correction applied to ensure Euclidean distance matrices and significance assessed using 10,000 permutations. To complement PACo, we applied ParaFit using the ade4 package, again using Cailliez-corrected host and parasite distance matrices and 9,999 permutations to obtain ParaFitGlobal and ParaFitLink statistics. Both analyses were performed on the trimmed natural matrix to ensure compatibility with the underlying algorithms.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Identification of FBN30 orthologs and phylogenetic reconstruction\u003c/h2\u003e \u003cp\u003eUsing the \u003cem\u003eAnopheles gambiae\u003c/em\u003e FBN30 sequence as a reference, 29 one-to-one FBN30 orthologs across \u003cem\u003eAnopheles\u003c/em\u003e species has been identified, with a single ortholog recovered from \u003cem\u003eCulex quinquefasciatus\u003c/em\u003e as an outgroup. All sequences contained an intact fibrinogen-like (FBG) domain, confirming orthology and functional conservation. After alignment and trimming, the final dataset comprised 274 amino-acid positions and 747 codon sites. Maximum-likelihood phylogenetic inference recovered a topology broadly congruent with established \u003cem\u003eAnopheles\u003c/em\u003e relationships, with strong branch support across most nodes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The resulting tree was used as a fixed topology for all subsequent evolutionary analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Lineage-specific episodic diversifying selection on FBN30\u003c/h2\u003e \u003cp\u003eTo assess whether FBN30 experienced adaptive evolution, branch- and site-based codon models has been applied. aBSREL detected significant episodic diversifying selection on a single branch corresponding to the \u003cem\u003eAnopheles darlingi\u003c/em\u003e lineage, with no other branches showing evidence of ω\u0026thinsp;\u0026gt;\u0026thinsp;1. Along this lineage, approximately 24% of sites were inferred to evolve under strong positive selection, indicating a localized episode of adaptive divergence rather than widespread relaxation of constraint (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Consistent with this result, branch-site analysis using codeml supported positive selection on the \u003cem\u003eA. darlingi\u003c/em\u003e branch, yielding a significantly better fit than the null model (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Together, these analyses identify \u003cem\u003eA. darlingi\u003c/em\u003e FBN30 as the sole lineage exhibiting detectable episodic adaptive evolution.\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\u003eSummary of molecular evolution analyses identifying episodic and site-specific positive selection in\u003c/b\u003e \u003cb\u003eAnopheles\u003c/b\u003e \u003cb\u003eFBN30 orthologs.\u003c/b\u003e This table presents the results of aBSREL, the branch-site codeml model, and MEME analysis. The aBSREL test detected a single branch under episodic diversifying selection, corresponding to \u003cem\u003eA. darlingi\u003c/em\u003e FBN30 (ADAR2_011252_R18153). The branch-site model similarly supported positive selection on this lineage, with one codon site (*) showing elevated posterior probabilities (BEB\u0026thinsp;\u0026ge;\u0026thinsp;0.95). MEME analysis identified codon 173 (corresponding to residue 218 in \u003cem\u003eA. darlingi\u003c/em\u003e and residue 191 in Node 26) as evolving under episodic positive selection with a significant LRT. The combined results support the presence of a lineage-specific adaptive substitution along the \u003cem\u003eA. darlingi\u003c/em\u003e branch.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKey Result\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStatistical Support\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNotes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaBSREL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eA. darlingi\u003c/em\u003e branch under episodic diversifying selection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.0; ω₂ = 57.25 at 23.6% sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong evidence for branch-specific selection\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBranch-site codeml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeveral sites under selection in \u003cem\u003eA. darlingi\u003c/em\u003e foreground\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLRT\u0026thinsp;=\u0026thinsp;6.72; p\u0026thinsp;=\u0026thinsp;0.01\u003c/p\u003e \u003cp\u003eBEB\u0026thinsp;\u0026ge;\u0026thinsp;0.969* at codon 51; BEB\u0026thinsp;\u0026ge;\u0026thinsp;0.842 at codon 226; moderate support at others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConfirms aBSREL signal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMEME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCodon 173 under episodic selection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLRT\u0026thinsp;=\u0026thinsp;4.63; p\u0026thinsp;=\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSelected residue maps to FBN30 FBG domain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Site-level selection and localization within the FBG domain\u003c/h2\u003e \u003cp\u003eSite-level tests using MEME identified a single codon evolving under episodic positive selection specifically along the \u003cem\u003eA. darlingi\u003c/em\u003e lineage (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This codon maps to a residue within the conserved FBG domain, a region implicated in ligand recognition across fibrinogen-related proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Although codeml and MEME differed in the specific sites receiving highest posterior support-reflecting their distinct statistical sensitivities, both approaches converged on the conclusion that adaptive evolution in FBN30 is limited in scope and concentrated within a functionally relevant domain.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Ancestral state reconstruction reveals derived substitution in A. darlingi\u003c/h2\u003e \u003cp\u003eAncestral sequence reconstruction was performed to place the positively selected site in an evolutionary context. The reconstructed ancestor shared by \u003cem\u003eA. darlingi\u003c/em\u003e and its sister taxon \u003cem\u003eA. aquasalis\u003c/em\u003e carried a serine at the selected position (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), whereas the extant \u003cem\u003eA. darlingi\u003c/em\u003e sequence contains an asparagine (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This serine-to-asparagine substitution therefore represents a derived change unique to the \u003cem\u003eA. darlingi\u003c/em\u003e lineage and was used as a focal point for downstream structural analyses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eRosetta ΔΔG stability results for extant\u003c/b\u003e \u003cb\u003eA. darlingi\u003c/b\u003e \u003cb\u003eFBN30, the N218S mutant, and the reconstructed Node 26 ancestor.\u003c/b\u003e Rosetta FastRelax was performed with ten independent replicates to estimate the energetic consequences of the selected substitution. The extant \u003cem\u003eA. darlingi\u003c/em\u003e FBN30 exhibited more favorable energies than the Node 26 ancestor in all replicates, whereas the N218S mutation introduced into the extant protein produced moderate and variable destabilization.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean ΔΔG (REU)\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNotes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN218S (relative to WT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.93\u0026thinsp;\u0026plusmn;\u0026thinsp;7.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMild destabilization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHighly variable between replicates\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNode 26 ancestor (relative to WT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e237.62\u0026thinsp;\u0026plusmn;\u0026thinsp;14.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStrong destabilization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndicates globally less stable ancestral fold\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Structural stability reflects fine-scale evolutionary modulation\u003c/h2\u003e \u003cp\u003eTo evaluate whether the derived substitution produced major structural effects, predicted protein stability across extant, ancestral, and back-mutated FBN30 variants was compared (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The reconstructed ancestral protein exhibited substantially reduced predicted stability relative to the extant \u003cem\u003eA. darlingi\u003c/em\u003e protein, reflecting cumulative divergence across the lineage rather than the effect of any single substitution. In contrast, introducing the ancestral residue into the extant background produced only modest and variable stability changes, indicating that the positively selected site contributes to localized modulation rather than global fold alteration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Evolutionary divergence is associated with altered predicted ligand engagement\u003c/h2\u003e \u003cp\u003eTo explore potential functional consequences of FBN30 evolution, the study used protein-protein docking and binding affinity prediction as a proxy for ligand engagement (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Comparative analyses revealed that the reconstructed ancestral FBN30 consistently exhibited stronger predicted affinity for \u003cem\u003ePlasmodium falciparum\u003c/em\u003e α-tubulin-1 than the extant \u003cem\u003eA. darlingi\u003c/em\u003e protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This difference was robust across docking metrics and binding energy estimates (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u0026amp; \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Importantly, the derived substitution alone did not account for the full magnitude of the observed affinity reduction, suggesting that evolutionary divergence along the \u003cem\u003eA. darlingi\u003c/em\u003e lineage involved cumulative changes affecting a parasite-facing interface.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eHADDOCK docking statistics for FBN30 variants interacting with\u003c/b\u003e \u003cb\u003ePlasmodium falciparum\u003c/b\u003e \u003cb\u003eα-tubulin-1.\u003c/b\u003e This table summarizes the best-scoring docked clusters for each FBN30 variant. The Node 26 ancestral sequence exhibited the most favorable docking energetics, lower RMSD, and greater buried surface area compared to the WT and N218S forms. These patterns are consistent with stronger and more stable binding in the ancestral FBN30-tubulin complex.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBest Cluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHADDOCK Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCluster Size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRMSD (\u0026Aring;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBuried Surface Area (\u0026Aring;\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZ-score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCluster 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e125.7\u0026thinsp;\u0026plusmn;\u0026thinsp;22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2202.0\u0026thinsp;\u0026plusmn;\u0026thinsp;98.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;1.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNode 26 ancestor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCluster 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e62.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2834.4\u0026thinsp;\u0026plusmn;\u0026thinsp;38.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN218S mutant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCluster 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e127.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e22.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2194.8\u0026thinsp;\u0026plusmn;\u0026thinsp;29.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;1.9\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\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003ePRODIGY binding free energy predictions (ΔG) for the interaction between FBN30 variants and\u003c/b\u003e \u003cb\u003eP. falciparum\u003c/b\u003e \u003cb\u003eα-tubulin-1.\u003c/b\u003e Binding energies were computed for all clusters (40 structures per variant). Node 26 exhibited significantly stronger binding than WT, whereas N218S did not differ significantly from WT.\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=\"left\" 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=\"left\" 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\u003eComparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean ΔG (kcal\u0026middot;mol⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDifference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatistical Test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWT vs Node 26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;12.16 vs \u0026minus;\u0026thinsp;13.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;1.71 kcal\u0026middot;mol⁻\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMann-Whitney U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.52 \u0026times; 10⁻\u0026sup1;⁰\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNode 26 binds significantly more strongly\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWT vs N218S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;12.16 vs \u0026minus;\u0026thinsp;12.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.34 kcal\u0026middot;mol⁻\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMann-Whitney U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo significant effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Global but not pairwise host-parasite phylogenetic congruence\u003c/h2\u003e \u003cp\u003eCophylogenetic analyses using PACo and ParaFit revealed significant global congruence between \u003cem\u003eAnopheles\u003c/em\u003e and \u003cem\u003ePlasmodium\u003c/em\u003e phylogenies based on documented natural associations. However, sparse host\u0026ndash;parasite linkages precluded reliable identification of individual coevolving pairs. These results indicate broad phylogenetic structuring of vector-parasite associations rather than strict pairwise co-speciation, consistent with lineage-specific rather than system-wide antagonistic evolution.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eFREPs are central components of mosquito innate immunity, yet their evolutionary dynamics and functional diversification remain incompletely understood. By integrating codon-based evolutionary analyses, ancestral sequence reconstruction, and structural modeling, this study identifies a discrete episode of episodic diversifying selection acting on FBN30 in the \u003cem\u003eA. darlingi\u003c/em\u003e lineage (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This adaptive signal is restricted to a single mosquito lineage and absent from other \u003cem\u003eAnopheles\u003c/em\u003e species, indicating localized evolutionary refinement rather than widespread diversification across the FREP family. Both branch-level and site-level analyses converge on this conclusion, with the positively selected residue mapping to the conserved FBG domain (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e), a region implicated in ligand recognition in fibrinogen-related proteins. The confinement of adaptive evolution to a single lineage suggests that FBN30 has been shaped by lineage-specific selective pressures, potentially reflecting geographically or ecologically structured host\u0026ndash;parasite interactions.\u003c/p\u003e \u003cp\u003eAncestral sequence reconstruction places the derived substitution in a clear evolutionary context, revealing a serine-to-asparagine change unique to \u003cem\u003eA. darlingi\u003c/em\u003e. Structural stability analyses indicate that this substitution produces only modest and localized effects, whereas the reconstructed ancestral protein differs substantially in overall predicted stability (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This pattern suggests that the adaptive substitution represents fine-scale functional tuning occurring on a background of broader evolutionary divergence, rather than a single mutation driving major structural reorganization. Such incremental refinement is consistent with expectations for proteins subject to ongoing functional constraint, where adaptive change is accommodated through subtle modulation rather than wholesale innovation.\u003c/p\u003e \u003cp\u003eTo explore potential functional consequences of this evolutionary divergence, protein-protein docking and binding affinity prediction as proxies for ligand engagement has been employed. These analyses consistently indicate that the reconstructed ancestral FBN30 exhibits stronger predicted affinity for \u003cem\u003ePlasmodium falciparum\u003c/em\u003e α-tubulin-1 than the extant \u003cem\u003eA. darlingi\u003c/em\u003e protein (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u0026amp; \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Although docking-based predictions cannot establish biochemical interaction, the concordance between evolutionary signal and predicted functional modulation suggests that adaptive evolution in FBN30 may have altered a parasite-facing interface. Notably, the derived substitution alone does not account for the full reduction in predicted affinity, implying that cumulative evolutionary changes along the \u003cem\u003eA. darlingi\u003c/em\u003e lineage contribute to this effect.\u003c/p\u003e \u003cp\u003eTaken together, these findings are consistent with a model in which inhibitory mosquito proteins undergo lineage-specific adaptive refinement under parasite-mediated selective pressures. Rather than indicating the emergence or loss of function, the observed evolutionary pattern suggests modulation of an existing recognition interface, potentially altering the strength or outcome of parasite engagement. Such a scenario aligns with theoretical expectations of antagonistic host-parasite evolution, where reciprocal pressures favor incremental adjustments rather than dramatic functional shifts.\u003c/p\u003e \u003cp\u003eCophylogenetic analyses reveal significant global congruence between \u003cem\u003eAnopheles\u003c/em\u003e and \u003cem\u003ePlasmodium\u003c/em\u003e phylogenies, supporting the view that vector-parasite associations are shaped by broad evolutionary constraints. However, the absence of detectable pairwise co-speciation underscores that adaptive events such as those observed in \u003cem\u003eA. darlingi\u003c/em\u003e FBN30 likely represent localized evolutionary responses rather than system-wide reciprocal evolution. This distinction highlights the importance of integrating lineage-specific molecular analyses with broader phylogenetic context when interpreting host\u0026ndash;parasite evolution.\u003c/p\u003e \u003cp\u003eSeveral limitations of this study warrant consideration. All functional inferences are based on in silico predictions and should be interpreted as hypotheses rather than demonstrations of molecular interaction. Moreover, the focus on a single mosquito lineage limits generalization across \u003cem\u003eAnopheles\u003c/em\u003e. Nevertheless, the convergence of evolutionary, structural, and predictive functional analyses provides a coherent framework for understanding how inhibitory mosquito proteins may be shaped by parasite-mediated selection.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study identifies lineage-specific adaptive evolution in FBN30 and suggests that fine-scale evolutionary refinement of a conserved recognition domain may modulate predicted parasite engagement. By emphasizing evolutionary inference over mechanistic assertion, this work contributes to a growing understanding of how host innate immune factors diversify under antagonistic interactions and generates testable hypotheses for future experimental investigation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding details\u003c/p\u003e\n\u003cp\u003eThis research received no funding.\u003c/p\u003e\n\u003cp\u003eConflicts of interest\u003c/p\u003e\n\u003cp\u003eThe author report that there are no competing interests to declare.\u003c/p\u003e\n\u003cp\u003eData availability statement\u003c/p\u003e\n\u003cp\u003eAll data associated with this study are available on Figshare (https://doi.org/10.6084/m9.figshare.30618974). The materials provided include both raw and processed datasets, as well as all scripts required to reproduce the analyses described in the manuscript. Supplementary files contained within the repository and the manuscript ensure full transparency and reproducibility of the work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eI thank the global open-access community for their commitment to freely accessible science, without which this study would not have been possible. I gratefully acknowledge VectorBase for providing high-quality genomic resources essential to this work. I also recognize the contributions of openly available language models that assisted in refining the manuscript. Finally, I am deeply grateful to my wife, Dr. Nabanita Ghosh, Assistant Professor of Zoology at Maulana Azad College, Kolkata, whose insightful discussions helped shape the central idea of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDaily, J. P. \u0026amp; Parikh, S. Malaria. \u003cem\u003eNew England Journal of Medicine\u003c/em\u003e\u003cstrong\u003e392\u003c/strong\u003e, 1320\u0026ndash;1333 (2025).\u003c/li\u003e\n\u003cli\u003eShaw, W. R., Marcenac, P. \u0026amp; Catteruccia, F. Plasmodium development in Anopheles: a tale of shared resources. \u003cem\u003eTrends Parasitol.\u003c/em\u003e\u003cstrong\u003e38\u003c/strong\u003e, 124 (2021).\u003c/li\u003e\n\u003cli\u003eZhang, G. \u003cem\u003eet al.\u003c/em\u003e Anopheles Midgut FREP1 Mediates Plasmodium Invasion. \u003cem\u003eJ. Biol. Chem.\u003c/em\u003e\u003cstrong\u003e290\u003c/strong\u003e, 16490 (2015).\u003c/li\u003e\n\u003cli\u003eNiu, G. \u003cem\u003eet al.\u003c/em\u003e The fibrinogen-like domain of FREP1 protein is a broad-spectrum malaria transmission-blocking vaccine antigen. \u003cem\u003eJournal of Biological Chemistry\u003c/em\u003e\u003cstrong\u003e292\u003c/strong\u003e, 11960\u0026ndash;11969 (2017).\u003c/li\u003e\n\u003cli\u003eZhang, G. \u003cem\u003eet al.\u003c/em\u003e Targeting plasmodium \u0026alpha;-tubulin-1 to block malaria transmission to mosquitoes. \u003cem\u003eFront. Cell. Infect. Microbiol.\u003c/em\u003e\u003cstrong\u003e13\u003c/strong\u003e, 1132647 (2023).\u003c/li\u003e\n\u003cli\u003eZhang, G., Niu, G., Perez, L., Wang, X. \u0026amp; Li, J. Malaria transmission assisted by interaction between Plasmodium \u0026alpha;-tubulin-1 and Anopheles FREP1 protein. \u003cem\u003ebioRxiv\u003c/em\u003e 2019.12.16.878082 (2019) doi:10.1101/2019.12.16.878082.\u003c/li\u003e\n\u003cli\u003eHanington, P. C. \u0026amp; Zhang, S. M. The Primary Role of Fibrinogen-Related Proteins in Invertebrates Is Defense, Not Coagulation. \u003cem\u003eJ. Innate Immun.\u003c/em\u003e\u003cstrong\u003e3\u003c/strong\u003e, 17 (2010).\u003c/li\u003e\n\u003cli\u003eMolina-Cruz, A. \u003cem\u003eet al.\u003c/em\u003e Some strains of Plasmodium falciparum, a human malaria parasite, evade the complement-like system of Anopheles gambiae mosquitoes. \u003cem\u003eProc. Natl. Acad. Sci. U. S. A.\u003c/em\u003e\u003cstrong\u003e109\u003c/strong\u003e, (2012).\u003c/li\u003e\n\u003cli\u003eLi, J. \u003cem\u003eet al.\u003c/em\u003e Genome-block expression-assisted association studies discover malaria resistance genes in Anopheles gambiae. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e\u003cstrong\u003e110\u003c/strong\u003e, 20675\u0026ndash;20680 (2013).\u003c/li\u003e\n\u003cli\u003eNiu, G. \u003cem\u003eet al.\u003c/em\u003e FBN30 in wild Anophelesgambiae functions as a pathogen recognition molecule against clinically circulating Plasmodiumfalciparum in malaria endemic areas in Kenya. \u003cem\u003eSci. Rep.\u003c/em\u003e\u003cstrong\u003e7\u003c/strong\u003e, 8577 (2017).\u003c/li\u003e\n\u003cli\u003eJanjoter, S. \u003cem\u003eet al.\u003c/em\u003e Deciphering the molecular targets of Plasmodium and Anopheline interactions for malaria control. \u003cem\u003eFEBS Journal\u003c/em\u003e https://doi.org/10.1111/FEBS.70250;WEBSITE:WEBSITE:FEBS;PAGE:STRING:ARTICLE/CHAPTER (2025) doi:10.1111/FEBS.70250;WEBSITE:WEBSITE:FEBS;PAGE:STRING:ARTICLE/CHAPTER.\u003c/li\u003e\n\u003cli\u003eGiraldo-Calder\u0026oacute;n, G. I. \u003cem\u003eet al.\u003c/em\u003e VectorBase: an updated bioinformatics resource for invertebrate vectors and other organisms related with human diseases. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e\u003cstrong\u003e43\u003c/strong\u003e, D707 (2014).\u003c/li\u003e\n\u003cli\u003eMoreno-Hagelsieb, G. \u0026amp; Latimer, K. Choosing BLAST options for better detection of orthologs as reciprocal best hits. \u003cem\u003eBioinformatics\u003c/em\u003e\u003cstrong\u003e24\u003c/strong\u003e, 319\u0026ndash;324 (2008).\u003c/li\u003e\n\u003cli\u003eHern\u0026aacute;ndez-Salmer\u0026oacute;n, J. E. \u0026amp; Moreno-Hagelsieb, G. Progress in quickly finding orthologs as reciprocal best hits: comparing blast, last, diamond and MMseqs2. \u003cem\u003eBMC Genomics 2020 21:1\u003c/em\u003e\u003cstrong\u003e21\u003c/strong\u003e, 741- (2020).\u003c/li\u003e\n\u003cli\u003eJones, P. \u003cem\u003eet al.\u003c/em\u003e InterProScan 5: genome-scale protein function classification. \u003cem\u003eBioinformatics\u003c/em\u003e\u003cstrong\u003e30\u003c/strong\u003e, 1236\u0026ndash;1240 (2014).\u003c/li\u003e\n\u003cli\u003eL\u0026ouml;ytynoja, A. Phylogeny-Aware Alignment with PRANK and PAGAN. \u003cem\u003eMethods Mol. Biol.\u003c/em\u003e\u003cstrong\u003e2231\u003c/strong\u003e, 17\u0026ndash;37 (2021).\u003c/li\u003e\n\u003cli\u003eSuyama, M., Torrents, D. \u0026amp; Bork, P. PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e\u003cstrong\u003e34\u003c/strong\u003e, W609 (2006).\u003c/li\u003e\n\u003cli\u003eSteenwyk, J. L., Buida, T. J., Li, Y., Shen, X. X. \u0026amp; Rokas, A. ClipKIT: A multiple sequence alignment trimming software for accurate phylogenomic inference. \u003cem\u003ePLoS Biol.\u003c/em\u003e\u003cstrong\u003e18\u003c/strong\u003e, e3001007 (2020).\u003c/li\u003e\n\u003cli\u003eWong, T. K. F. \u003cem\u003eet al.\u003c/em\u003e IQ-TREE 3: Phylogenomic Inference Software using Complex Evolutionary Models. https://doi.org/10.32942/X2P62N (2025) doi:10.32942/X2P62N.\u003c/li\u003e\n\u003cli\u003eKalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., Von Haeseler, A. \u0026amp; Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. \u003cem\u003eNature Methods 2017 14:6\u003c/em\u003e\u003cstrong\u003e14\u003c/strong\u003e, 587\u0026ndash;589 (2017).\u003c/li\u003e\n\u003cli\u003eSinha, K. Episodic Positive Selection Signatures in Arabidopsis CONSTANS-like Genes COL3 and COL5 Indicating Adaptive Evolution in Red-Light Signaling Pathways. \u003cem\u003ebioRxiv\u003c/em\u003e 2025.04.03.646976 (2025) doi:10.1101/2025.04.03.646976.\u003c/li\u003e\n\u003cli\u003eSinha, K., Jana, S., Pramanik, P. \u0026amp; Bera, B. Selection on synonymous codon usage in soybean (Glycine max) WRKY genes. \u003cem\u003eScientific Reports 2024 14:1\u003c/em\u003e\u003cstrong\u003e14\u003c/strong\u003e, 26530- (2024).\u003c/li\u003e\n\u003cli\u003eSmith, M. D. \u003cem\u003eet al.\u003c/em\u003e Less Is More: An Adaptive Branch-Site Random Effects Model for Efficient Detection of Episodic Diversifying Selection. \u003cem\u003eMol. Biol. Evol.\u003c/em\u003e\u003cstrong\u003e32\u003c/strong\u003e, 1342 (2015).\u003c/li\u003e\n\u003cli\u003eKosakovsky Pond, S. L. \u003cem\u003eet al.\u003c/em\u003e HyPhy 2.5\u0026mdash;A Customizable Platform for Evolutionary Hypothesis Testing Using Phylogenies. \u003cem\u003eMol. Biol. Evol.\u003c/em\u003e\u003cstrong\u003e37\u003c/strong\u003e, 295 (2019).\u003c/li\u003e\n\u003cli\u003eYang, Z. PAML 4: Phylogenetic Analysis by Maximum Likelihood. \u003cem\u003eMol. Biol. Evol.\u003c/em\u003e\u003cstrong\u003e24\u003c/strong\u003e, 1586\u0026ndash;1591 (2007).\u003c/li\u003e\n\u003cli\u003e\u0026Aacute;lvarez-Carretero, S., Kapli, P. \u0026amp; Yang, Z. Beginner\u0026rsquo;s Guide on the Use of PAML to Detect Positive Selection. \u003cem\u003eMol. Biol. Evol.\u003c/em\u003e\u003cstrong\u003e40\u003c/strong\u003e, (2023).\u003c/li\u003e\n\u003cli\u003eJumper, J. \u003cem\u003eet al.\u003c/em\u003e Highly accurate protein structure prediction with AlphaFold. \u003cem\u003eNature 2021 596:7873\u003c/em\u003e\u003cstrong\u003e596\u003c/strong\u003e, 583\u0026ndash;589 (2021).\u003c/li\u003e\n\u003cli\u003eLarsson, A. AliView: a fast and lightweight alignment viewer and editor for large datasets. \u003cem\u003eBioinformatics\u003c/em\u003e\u003cstrong\u003e30\u003c/strong\u003e, 3276\u0026ndash;3278 (2014).\u003c/li\u003e\n\u003cli\u003eSora, V. \u003cem\u003eet al.\u003c/em\u003e RosettaDDGPrediction for high‐throughput mutational scans: From stability to binding. \u003cem\u003eProtein Sci.\u003c/em\u003e\u003cstrong\u003e32\u003c/strong\u003e, e4527 (2023).\u003c/li\u003e\n\u003cli\u003eHonorato, R. V. \u003cem\u003eet al.\u003c/em\u003e The HADDOCK2.4 web server for integrative modeling of biomolecular complexes. \u003cem\u003eNature Protocols 2024 19:11\u003c/em\u003e\u003cstrong\u003e19\u003c/strong\u003e, 3219\u0026ndash;3241 (2024).\u003c/li\u003e\n\u003cli\u003eZhang, G. \u003cem\u003eet al.\u003c/em\u003e Targeting plasmodium \u0026alpha;-tubulin-1 to block malaria transmission to mosquitoes. \u003cem\u003eFront. Cell. Infect. Microbiol.\u003c/em\u003e\u003cstrong\u003e13\u003c/strong\u003e, 1132647 (2023).\u003c/li\u003e\n\u003cli\u003eXue, L. C., Rodrigues, J. P., Kastritis, P. L., Bonvin, A. M. \u0026amp; Vangone, A. PRODIGY: a web server for predicting the binding affinity of protein\u0026ndash;protein complexes. \u003cem\u003eBioinformatics\u003c/em\u003e\u003cstrong\u003e32\u003c/strong\u003e, 3676\u0026ndash;3678 (2016).\u003c/li\u003e\n\u003cli\u003eVangone, A. \u0026amp; Bonvin, A. M. J. J. Contacts-based prediction of binding affinity in protein\u0026ndash;protein complexes. \u003cem\u003eElife\u003c/em\u003e\u003cstrong\u003e4\u003c/strong\u003e, (2015).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Anopheles darlingi, Plasmodium falciparum, FBN30, α-tubulin-1, ookinete","lastPublishedDoi":"10.21203/rs.3.rs-8743126/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8743126/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFibrinogen-related proteins (FREPs) contribute to mosquito-parasite interactions, yet the evolutionary processes shaping their functional diversification remain poorly resolved. The mosquito protein FBN30 has been implicated in restricting \u003cem\u003ePlasmodium\u003c/em\u003e development, but its molecular basis of action is unknown. Here, the study examines the evolutionary history of FBN30 across \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes to test whether lineage-specific adaptive evolution has modified its functional properties. Codon-based analyses of FBN30 orthologs from 29 \u003cem\u003eAnopheles\u003c/em\u003e species reveal a single episode of strong episodic diversifying selection confined to the \u003cem\u003eAnopheles darlingi\u003c/em\u003e lineage. Site-level tests identify a positively selected residue within the conserved fibrinogen-like (FBG) domain. Ancestral sequence reconstruction shows that this site underwent a serine-to-asparagine substitution along the \u003cem\u003eA. darlingi\u003c/em\u003e lineage, with structural modeling indicating only modest local effects on protein stability. Using protein-protein docking and binding affinity prediction as a proxy for functional engagement, the study finds that the reconstructed ancestral FBN30 exhibits significantly stronger predicted affinity for \u003cem\u003ePlasmodium falciparum\u003c/em\u003e α-tubulin-1 than the extant \u003cem\u003eA. darlingi\u003c/em\u003e protein, whereas the derived substitution alone does not account for this difference. These results indicate that evolutionary divergence in FBN30 is associated with reduced predicted engagement at a parasite-facing interface and support a model in which inhibitory mosquito proteins undergo fine-scale adaptive refinement under parasite-mediated selective pressures.\u003c/p\u003e","manuscriptTitle":"Lineage-Specific Adaptive Evolution of the Mosquito Fibrinogen-Related Protein FBN30 at a Predicted Parasite-Facing Interface","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-13 05:20:05","doi":"10.21203/rs.3.rs-8743126/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"38941789873217998172751041785523300793","date":"2026-05-16T15:45:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197404046989314723737647657165029027564","date":"2026-05-11T14:58:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-01T04:02:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139597745979272671010923968406236581173","date":"2026-02-12T19:37:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216689974250372588010852800897083090513","date":"2026-02-09T22:55:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-07T17:12:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-07T16:54:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-03T12:06:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-02T08:08:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-02T07:41:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"980c0f5c-3529-4056-8232-768d48b43b9e","owner":[],"postedDate":"February 13th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"38941789873217998172751041785523300793","date":"2026-05-16T15:45:00+00:00","index":66,"fulltext":""},{"type":"reviewerAgreed","content":"197404046989314723737647657165029027564","date":"2026-05-11T14:58:32+00:00","index":63,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62837657,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":62837658,"name":"Biological sciences/Evolution"}],"tags":[],"updatedAt":"2026-02-13T05:20:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-13 05:20:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8743126","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8743126","identity":"rs-8743126","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}