LAUSR

Directed evolution has emerged as a powerful strategy to engineer various properties of proteins. Traditional methods to construct libraries such as error-prone PCR and DNA shuffling commonly produce large, relatively inefficient libraries. In the absence of a high-throughput screening method, searching such libraries is time-consuming, laborious and costly. On the other hand, targeted mutagenesis guided by structure or sequence information has become a popular way to produce so-called smart libraries. With an increased ratio of advantageous to deleterious mutations, smart libraries increase the efficiency of directed evolution, provided that target site prediction is reliable. Mutation target site or hot spot prediction is critical to the quality of libraries and the performance of directed evolution. Appropriate selection of hot spots enables the generation of proteins with desired properties efficiently and rationally. Here, we give an overview of seven kinds of hot spots that are divided into two categories: sequence-based hot spots including CbD (conserved but different) sites and coevolving residues, and then 3D structure-based hot spots including active-site residues, access tunnel sites, flexible sites, distal sites coupled to active center, and interface sites. This review also covers the latest advances in computational tools for identifying these hot spots and many successful cases using them for enzyme engineering.