Evolution of Phosphorylase Activity in an Ancestral Glycosyltransferase

Abstract

The reconstruction of ancestral sequences can offer a glimpse into the fascinating process of molecular evolution by exposing the adaptive pathways that shape the proteins found in nature today. Here, we track the evolution of the carbohydrate-active enzymes responsible for the synthesis and turnover of mannogen, a critical carbohydrate reserve in Leishmania parasites. Biochemical characterization of resurrected enzymes demonstrated that mannoside phosphorylase activity emerged in an ancestral bacterial mannosyltransferase, and later disappeared in the process of horizontal gene transfer and gene duplication in Leishmania. By shuffling through plausible historical sequence space in an ancestral mannosyltransferase, we found that mannoside phosphorylase activity could be toggled on through various combinations of mutations at positions outside of the active site. Molecular dynamics simulations showed that such mutations can affect loop rigidity and shield the active site from water molecules that disrupt key interactions, allowing α-mannose 1-phosphate to adopt a catalytically productive conformation. These findings highlight the importance of subtle distal mutations in protein evolution and suggest that the vast collection of natural glycosyltransferases may be a promising source of engineering templates for the design of tailored phosphorylases.

The sugar donor specificity of plant family 1 glycosyltransferases

“…This could potentially contribute to a more comprehensive understanding of the structure-function relationship, particularly in terms of what determines the specificity of enzymes that share similar substrates. Other research directions could include in silico studies such as molecular dynamics simulations with the N5 loop being a potential target of these studies or ancestral sequence reconstruction to uncover a potential evolutionary element of the structure-function relationship ( Franceus et al, 2024 ). Lastly, the emergence of machine learning and explainable artificial intelligence as tools to uncover enzyme mechanisms and structure-function relationships could also prove useful ( Taujale et al, 2020 ; Kouba et al, 2023 ).…”