Enhancing modern proteins with ancient insights: the Power of Ancestral Sequence Reconstruction

Abstract

Ancestral sequence reconstruction (ASR) has emerged as a powerful approach in protein engineering, offering unique insights into evolutionary biochemistry while enabling the creation of proteins with enhanced stability, specificity, and multifunctionality. This review provides a comprehensive overview of ASR in protein engineering, detailing its methodology, applications, and advantages over consensus sequence design. It highlights the stepwise ASR workflow, including sequence collection, multiple sequence alignment, phylogenetic tree construction, ancestral inference, in silico filtration, and experimental validation. Case studies illustrate ASR's effectiveness in enhancing thermostability, solubility, expression, and multifunctionality, as well as its utility as a template for further engineering. Comparative analysis reveals ASR’s superior ability to preserve functional epistatic interactions and evolutionary context, resulting in proteins outperforming consensus-designed variants. Advances in computational tools like AlphaFold and RoseTTAFold, are poised to refine ASR workflows and expand its applications. Challenges, including dataset quality, variability in inference methods, and experimental validation hurdles, are discussed alongside strategies to address them. Alternative strategies, including machine learning-driven models and evolutionary algorithm-based methods, present promising complements or potential substitutes for ASR, especially as predictive capabilities improve. This review underscores ASR’s impact in protein engineering, while outlining key areas for methodological refinement and integration with cutting-edge computational advancements, positioning ASR as an indispensable tool for next-generation enzyme design and synthetic biology applications.