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Computational enzyme redesign: large jumps in function
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DOI：10.1016/j.trechm.2022.03.001.png)
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Rising demands for enzymes in biotechnological applications have fueled efforts to tailor their properties towards desired functions, such as activity, selectivity, and stability. Computational methods are increasingly used in this task, providing designs that efficiently navigate large regions of sequence space with a greatly reduced experimental burden. With the improvement of enzyme redesign algorithms, model-based methods have achieved significant success in recent decades. Meanwhile, the rapid growth in protein databases has also promoted the development of data-driven approaches. Although data-driven approaches are just emerging, it will be exciting to see whether they can advance the field of enzyme redesign with the accumulation of more standard data, just as they are with structure prediction. Here, we present a brief overview of the field of computational enzyme redesign. We anticipate a marriage between model-based and data-based approaches which may offer opportunities to achieve more ambitious enzyme engineering goals in the coming years.
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Why computational enzyme design?
thermodynamic stability (Figure 1
Key figure) [3]
mental effort
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