Exploiting enzyme evolution for computational protein design

• Published in: Trends in biochemical sciences (Amsterdam. Regular ed.) 2022-05, Vol.47 (5), p.375-389
• Main Authors: Pinto, Gaspar P., Corbella, Marina, Demkiv, Andrey O., Kamerlin, Shina Caroline Lynn
• Format: Article
• Language: English
• Subjects: Computational Biology; conformational dynamics; directed evolution; enhanced sampling approaches; Evolution, Molecular; Proteins - genetics; residue coevolution; structural bioinformatics
• ISSN: 0968-0004; 1362-4326; 1362-4326
• DOI: 10.1016/j.tibs.2021.08.008

Recent years have seen an explosion of interest in understanding the physicochemical parameters that shape enzyme evolution, as well as substantial advances in computational enzyme design. This review discusses three areas where evolutionary information can be used as part of the design process: (i) using ancestral sequence reconstruction (ASR) to generate new starting points for enzyme design efforts; (ii) learning from how nature uses conformational dynamics in enzyme evolution to mimic this process in silico; and (iii) modular design of enzymes from smaller fragments, again mimicking the process by which nature appears to create new protein folds. Using showcase examples, we highlight the importance of incorporating evolutionary information to continue to push forward the boundaries of enzyme design studies. We can learn from nature’s tricks by reconstructing evolutionary trajectories to design improved enzymes.Ancestral sequence reconstruction (ASR) provides a compelling tool to obtain enzymes with customized catalytic properties.Conformational dynamics in enzyme design is crucial in increasing the sampling of states with new catalytic functions as well as reducing the sampling of non-productive conformations.A catalog of fragments characterized by specific biophysical features may provide an invaluable resource for the design of custom-made enzymes.