In silico design (ISD) methods are used to discover new biocatalysts and improve existing enzyme performance. Deep learning methods have been increasingly applied to find patterns in data favorable to predicting substrate specificity, and guiding rational design to improve enzyme stability, solubility, and other characteristics.
High throughput screening or selection methods plays an extremely significant role in successful evolutionary enzyme engineering, which greatly increases the chance of obtaining desired properties and reduce the time and cost. By taking advantage of automation, high throughput screening methods can streamline traditional screening process. Most importantly, designed methods can make the desired mutants easy to detect.
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