Engineering a highly active thermophilic F1-ATPase by homolog-guided exploration and machine-learning-assisted prioritization

Abstract

The rotary motor F1-ATPase has been extensively studied as a model molecular machine, yet rational engineering of its catalytic activity remains challenging because ATP hydrolysis is regulated by long-range intersubunit allostery and large conformational transitions. Here, we developed a homolog-guided engineering strategy to increase the maximum rotation rate of the thermophilic Bacillus PS3 F1-ATPase (TF1). Candidate mutation sites were first identified by comparing TF1 with the homologous enzymes bovine mitochondrial F1 (bMF1) and Paracoccus denitrificans F1 (PdF1), both of which exhibit higher maximum rotation rates than TF1. Systematic exploration of these sites identified four activity-enhancing hotspots, followed by focused hotspot exploration and machine-learning-assisted prioritization of combinatorial mutants. The best mutant, TF1({beta}Y313L/{beta}E332S), exhibited a 1.8-fold higher maximum rotation rate than TF1(WT) while retaining its functional thermostability. Interestingly, activity-enhancing substitutions were not limited to the residues conserved in both bMF1 and PdF1, indicating that the bMF1-PdF1 consensus substitutions effectively identify activity-enhancing hotspots rather than uniquely defining the optimal amino acid. Machine-learning-assisted exploration efficiently prioritized highly active mutants, although the predictive performance was limited by the relatively small training dataset and epistatic interactions among mutations. Kinetic and structural comparisons further provided mechanistic insights into the enhanced catalytic activity of the engineered mutant. Together, these results establish a practical strategy for engineering complex molecular motors by combining homolog-guided hotspot identification with focused hotspot exploration.