Integrating evolutionary signals and predicted protein structure reveals localized molecular divergence in Cereus lineages.

Understanding how positive selection shapes molecular evolution at the protein level remains a major challenge, particularly in non-model species. Here, we integrated phylogenetic evidence from candidate genes evolving under lineage-specific positive selection with comparative structural and biophysical analyses to investigate patterns of molecular divergence in the Cereus fernambucensis-C. insularis clade, which occupies contrasting island and continental environments. Based on a previous branch-site analysis, five orthogroups were selected for downstream analyses. Orthologous protein structures were predicted and compared, variability hotspots were identified, pockets proximity were evaluated, and the stability effects of lineage-specific substitutions were estimated. Across orthogroups, global folds were largely conserved, whereas molecular divergence was primarily associated with localized structural variation in flexible loops and surface-exposed regions. Variability hotspots were significantly enriched near predicted cavities in several orthogroups, indicating that molecular diversification was concentrated in structurally permissive regions frequently associated with putative interaction surfaces. Stability analyses of reconstructed branch-specific substitutions revealed heterogeneous energetic effects, ranging from stabilizing to strongly destabilizing changes, while global protein architecture remained largely conserved. Together, these results suggest that evolutionary divergence in Cereus lineages is primarily associated with localized modifications of surface-accessible regions rather than large-scale structural innovation. More broadly, our findings demonstrate how integrating molecular evolutionary analyses with structural bioinformatics provides a robust framework for interpreting candidate adaptive genes in non-model organisms, while highlighting both the potential and limitations of structure-informed evolutionary inference.