Improving the Thermostability of Enzymes Using Bioinformatics and Electrostatics Analysis



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Rational improvement of enzyme catalytic activity is one of the most significant challenges in biotechnology. Most conventional strategies to engineer enzymes involve selecting mutations to increase their thermostability. However, the criteria to perform residue substitutions are not clear. In this work, we combine bioinformatics, electrostatic analysis, and molecular dynamics to predict beneficial mutations that may improve the thermostability of xylanase A from Bacillus subtilis. First, the Tanford-Kirkwood Surface Accessibility method was used to characterize each ionizable residue's contribution to the protein native state stability. Then, residues identified to be destabilizing were mutated with the corresponding residues found in the consensus or ancestral sequences at the same locations. Five mutants were investigated and compared with several control mutants derived from experimental approaches. Molecular dynamics simulation results show the mutants exhibited folding temperatures higher than the wild type, which along with a set of controls corroborate with experimental findings. The combined approaches employed here are an effective strategy for low-cost enzyme optimization for large scale biotechnological and medical applications. This project was completed with contributions from Fernando Bruno and Vitor Leite from the São Paulo State University and Jose Onuchic and Vinicius Contessoto from Rice University.