This review presents computational methods that experimentalists can readily use to create smart libraries for enzyme engineering and to obtain insights into protein-substrate complexes. Computational tools have the reputation of being hard to use and inaccurate compared to experimental methods in enzyme engineering, yet they are essential to probe datasets of ever-increasing size and complexity. In recent years, bioinformatics groups have made a huge leap forward in providing user-friendly interfaces and accurate algorithms for experimentalists. These methods guide efficient experimental planning and allow the enzyme engineer to rationalize time and resources. Computational tools nevertheless face challenges in the realm of transient modern technology.
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