Quantum computing for faster enzyme discovery and engineering

Abstract

Quantum computing, by leveraging the unique principles of quantum mechanics, offers transformative potential for biocatalysis and related disciplines. Compared to classical algorithms, quantum algorithms deliver immense acceleration to quantum computers, making them suited for tackling computationally challenging problems such as simulating many-body biomolecular systems or enzyme-catalysed chemical reactions. However, current quantum hardware is constrained by noise, limited qubit coherence and high error rates, restricting its capacity to model complex biochemical phenomena. Here we explore the rapidly advancing landscape of quantum computing and its future applications in the discovery and rational engineering of biocatalysts. We identify key areas where quantum algorithms could surpass classical limitations, including the quantum chemistry-based design of biocatalysts with enhanced catalytic activity or selectivity, parallelized mining of novel enzymes, accurate ancestral sequence reconstruction, and combinatorial in silico protein evolution. Overcoming current hardware limitations could unlock transformative advances in both fundamental enzymology and industrial bioprocessing. Quantum computing is a promising technology to solve complex challenges that would take classical computers an impractical amount of time. This Perspective discusses the current state of quantum computing and possible applications in enzyme engineering and biocatalysis.