Protein Sequence Selection Method That Enables Full Consensus Design of Artificial l-Threonine 3-Dehydrogenases with Unique Enzymatic Properties

Abstract

Exponentially increasing protein sequence data enables artificial enzyme design using sequence-based protein design methods, including full-consensus protein design (FCD). The success of artificial enzyme design is strongly dependent on the nature of the sequences used. Hence, sequences must be selected from databases and curated libraries prepared to enable successful design by FCD. In this study, we proposed a selection approach by regarding several key residues as sequence motifs. We used L-threonine 3-dehydrogenase (TDH) as a model to test the validity of this approach. In the classification, four residues (143, 174, 188, and 214) were used as key residues. We classified thousands of TDH homologous sequences into five groups containing hundreds of sequences. Utilizing sequences in the libraries, we designed five artificial TDHs by FCD. Among the five, we successfully expressed four in soluble form. Biochemical analysis of the artificial TDHs indicated that their enzymatic properties vary; half of the maximum measured enzyme activity (t1/2) and activation energies were distributed from 53 to 65 °C and from 38 to 125 kJ/mol, respectively. The artificial TDHs had unique kinetic parameters, distinct from one another. Structural analysis indicates that consensus mutations are mainly introduced in the secondary or outer shell. The functional diversity of the artificial TDHs is due to the accumulation of mutations that affect their physicochemical properties. Taken together, our findings indicate that our proposed approach can help generate artificial enzymes with unique enzymatic properties.