To the Final Goal: Can We Predict and Suggest Mutations for Protein to Develop Desired Phenotype?

Abstract

Directed evolution of proteins is a good approach to develop desired phenotypes from existing proteins. Fully experimental protein evolution usually utilizes randomization of a given protein sequence by error-prone PCR or gene shuffling followed by high-throughput selection or timeconsuming screening method. However, these random methods create mutant library full of deleterious mutations. In addition, they need high-throughput screening or selection method to search for positive clones from an enormous size of mutant library. Construction of a mutant library while retaining the original function is important for efficient protein evolution because it greatly reduces time and effort for the identification of positive mutants. Therefore, researchers have tried to reduce the size of mutant library by minimizing the occurrence of deleterious mutants. Such efforts have led to the creation of a concept of ‘small but smart library’. For this goal, neutral drift theory has been applied. Although smart library greatly reduces the library size, it is still the beyond the capacity of low-throughput assay. In parallel, computational analysis of protein structure and efforts to discriminate mutatable residues from all residues of a given protein have been consistently pursued. Accumulated knowledge of protein evolution through random mutation and selection has improved our understanding of functions of amino acids in protein structure. Protein evolution by rational design is being developed based on such understanding. In this review, we describe how the use of semi-rationally designed library rather than completely random one has impacted the overall procedure of directed evolution. We also describe efforts made to evaluate the effect of single mutation. Such efforts will bring lazy boys to the final goal - computational mutation suggestion system.