Algorithm-based coevolution network identification reveals key functional residues of the α/β hydrolase subfamilies

Covariant residues identified by computational algorithms have provided new insights into enzyme evolutionary routes. However, the reliability and accuracy of routine statistical coupling analysis (SCA) are unable to satisfy the needs of protein engineering because SCA depends only on sequence information. Here, we set up a new SCA algorithm, SCA.SIM, by integrating structure information and MD simulation data. The more reliable covariant residues with high-quality scores are obtained from sequence alignment weighted by residual movement for eight related subfamilies, belonging to α/β hydrolase family, with Candida antarctica lipase B (CALB). The 38 predicted covariant residues are tested for function by high-throughput quantitative evaluation in combination with activity and thermostability assays of a mutant library and deep sequencing. Based on the landscapes of both activity and thermostability, most mutants play key roles in catalysis, and some mutants gain 2.4- to 6-fold increase in half-life at 50°C and 9- to 12-fold improvement in catalytic efficiency. The activity of double mutants for A225F/T103A is higher than those of A225F and T103A which means that SCA.SIM method might be useful for identifying the allosteric coupling. The SCA.SIM algorithm can be used for protein coevolution and enzyme engineering research.