RiSLnet: Rapid identification of smart mutant libraries using protein structure network. Application to thermal stability enhancement

Abstract

A key point of protein stability engineering is to identify specific target residues whose mutations can stabilize the protein structure without negatively affecting the function or activity of the protein. Here, we propose a method called RiSLnet (Rapid identification of Smart mutant Library using residue network) to identify such residues by combining network analysis for protein residue interactions, identification of conserved residues, and evaluation of relative solvent accessibility. To validate its performance, the method was applied to four proteins, that is, T4 lysozyme, ribonuclease H, barnase, and cold shock protein B. Our method predicted beneficial mutations in thermal stability with ~62% average accuracy when the thermal stability of the mutants was compared with the ones in the Protherm database. It was further applied to lysine decarboxylase (CadA) to experimentally confirm its accuracy and effectiveness. RiSLnet identified mutations increasing the thermal stability of CadA with the accuracy of ~60% and significantly reduced the number of candidate residues (~99%) for mutation. Finally, combinatorial mutations designed by RiSLnet and in silico saturation mutagenesis yielded a thermally stable triple mutant with the half-life (T 1/2 ) of 114.9 min at 58°C, which is approximately twofold higher than that of the wild-type.