title: Using Evolution to Guide Protein Engineering: The Devil IS in the Details.
abstract: For decades, protein engineers have endeavored to reengineer existing proteins for novel applications. Overall, protein folds and gross functions can be readily transferred from one protein to another by transplanting large blocks of sequence (i.e., domain recombination). However, predictably fine-tuning function (e.g., by adjusting ligand affinity, specificity, catalysis, and/or allosteric regulation) remains a challenge. One approach has been to use the sequences of protein families to identify amino acid positions that change during the evolution of functional variation. The rationale is that these nonconserved positions could be mutated to predictably fine-tune function. Evolutionary approaches to protein design have had some success, but the engineered proteins seldom replicate the functional performances of natural proteins. This Biophysical Perspective reviews several complexities that have been revealed by evolutionary and experimental studies of protein function. These include 1) challenges in defining computational and biological thresholds that define important amino acids; 2) the co-occurrence of many different patterns of amino acid changes in evolutionary data; 3) difficulties in mapping the patterns of amino acid changes to discrete functional parameters; 4) the nonconventional mutational outcomes that occur for a particular group of functionally important, nonconserved positions; 5) epistasis (nonadditivity) among multiple mutations; and 6) the fact that a large fraction of a protein's amino acids contribute to its overall function. To overcome these challenges, new goals are identified for future studies.
authors.affiliation: Department of Biochemistry and Molecular Biology, University of Kansas Medical Center, Kansas City, Kansas. Electronic address: lswint-kruse@kumc.edu.
authors.forename: Liskin
authors.initials: L
authors.lastname: Swint-Kruse
mesh_terms: D017124:Conserved Sequence; D019020:Directed Molecular Evolution; D019143:Evolution, Molecular; D009154:Mutation; D015202:Protein Engineering / Q000379:methods*; D011506:Proteins / Q000235:genetics* / Q000378:metabolism*
chemical_list: D011506:Proteins