Editing Protein engineering (section)

== Semi-rational design ==
Semi-rational design uses information about a proteins sequence, structure and function, in tandem with predictive algorithms. Together these are used to identify target amino acid residues which are most likely to influence protein function. Mutations of these key amino acid residues create libraries of mutant proteins that are more likely to have enhanced properties.<ref name=pmid20869867>{{cite journal |last1=Lutz |first1=Stefan |title=Beyond directed evolution—semi-rational protein engineering and design |journal=Current Opinion in Biotechnology |date=December 2010 |volume=21 |issue=6 |pages=734–743 |doi=10.1016/j.copbio.2010.08.011 |pmid=20869867 |pmc=2982887 }}</ref>

Advances in semi-rational enzyme engineering and de novo enzyme design provide researchers with powerful and effective new strategies to manipulate biocatalysts. Integration of sequence and structure based approaches in library design has proven to be a great guide for enzyme redesign. Generally, current computational de novo and redesign methods do not compare to evolved variants in catalytic performance. Although experimental optimization may be produced using directed evolution, further improvements in the accuracy of structure predictions and greater catalytic ability will be achieved with improvements in design algorithms. Further functional enhancements may be included in future simulations by integrating protein dynamics.<ref name=pmid20869867/>

Biochemical and biophysical studies, along with fine-tuning of predictive frameworks will be useful to experimentally evaluate the functional significance of individual design features. Better understanding of these functional contributions will then give feedback for the improvement of future designs.<ref name=pmid20869867/>

Directed evolution will likely not be replaced as the method of choice for protein engineering, although computational protein design has fundamentally changed the way protein engineering can manipulate bio-macromolecules. Smaller, more focused and functionally-rich libraries may be generated by using in methods which incorporate predictive frameworks for hypothesis-driven protein engineering. New design strategies and technical advances have begun a departure from traditional protocols, such as directed evolution, which represents the most effective strategy for identifying top-performing candidates in focused libraries. Whole-gene library synthesis is replacing shuffling and mutagenesis protocols for library preparation. Also highly specific low throughput screening assays are increasingly applied in place of monumental screening and selection efforts of millions of candidates. Together, these developments are poised to take protein engineering beyond directed evolution and towards practical, more efficient strategies for tailoring biocatalysts.<ref name=pmid20869867/>