Decoding hidden drivers of recurrent evolution in plants through the inference of amino acid ages.

Plants constantly face environmental challenges, impacting ecosystem stability, promoting new pathogens and compromising food security. A fundamental question in evolutionary biology is whether repeated adaptations, driven by similar environmental pressures, rely on the same genetic programs, especially over long timescales. Protein sequences, increasingly available across diverse lineages, offer a unique opportunity to tackle this issue on an unprecedented scale. However, as evolutionary divergence increases, current methods result in substantial information loss and an inherent bias due to reliance on a single optimal alignment configuration. This, in turn, makes it difficult to identify which substitutions among billions could convergently alter adaptive phenotypes.To overcome these challenges, I will establish a novel framework for inferring common evolutionary origins by incorporating suboptimal alignment configurations. Summarized as “amino acid ages”, I will date protein substitutions in a ladder-like fashion until a substitution is no longer stable in the suboptimal alignment space. The taxonomic level of the detected substitution will indicate the evolutionary origin/age of an amino acid within a protein. I hypothesize that amino acid ages capture emerging nuances missed in divergent lineages since suboptimal configurations may expose hidden evolutionary constraints.First, I will develop a fast and scalable computation of amino acid ages for community use. Second, I will generate a resource containing amino acid age annotations for all available plants. Third, I will establish associations between candidate protein substitutions that lack assigned ages, not acquired through common ancestry, and recurrent adaptive traits.RecAminoAge will provide the community with a disruptive methodology that sheds light on protein evolution rates over long timescales while enhancing predictions of species retaining valuable adaptations to tackle future challenges.