A longstanding goal in protein engineering is to identify specific sequence changes that endow proteins with desired functional properties. As opposed to traditional rational and random protein engineering techniques, we have employed a bioinformatic approach to identify specific sequence changes that influence key functional properties of a protein within a defined superfamily. Specifically, we have used the Bayesian sequence-based algorithms PROBE and Classifier to identify a strand-turn-strand motif that contributes to thermophilicity among members of the serine protease subtilase superfamily. By replacing a 16 amino acid sequence in the mesophilic subtilisin E (from Bacillus subtilis) with a bioinformatics-generated thermophilic model sequence, the melting temperature of subtilisin E was increased by 13 degrees C. While wild-type subtilisin E was inactive at 90 degrees C, the mutant retained a substantial fraction of its function, with ca. one-third of the activity that it has at 45 degrees C.