lipase (RML), as a kind of eukaryotic protein catalyst, plays an important role in the food, organic chemical, and biofuel industries. However, RML retains its catalytic activity below 50°C, which limits its industrial applications at higher temperatures. Soluble expression of this eukaryotic protein in not only helps to screen for thermostable mutants quickly but also provides the opportunity to develop rapid and effective ways to enhance the thermal stability of eukaryotic proteins. Therefore, in this study, RML was engineered using multiple computational design methods, followed by filtration via conservation analysis and functional region assessment. We successfully obtained a limited screening library (only 36 candidates) to validate thermostable single point mutants, among which 24 of the candidates showed higher thermostability and 13 point mutations resulted in an apparent melting temperature ([Formula: see text]) of at least 1°C higher. Furthermore, both of the two disulfide bonds predicted from four rational-design algorithms were further introduced and found to stabilize RML. The most stable mutant, with T18K/T22I/E230I/S56C-N63C/V189C-D238C mutations, exhibited a 14.3°C-higher [Formula: see text] and a 12.5-fold increase in half-life at 70°C. The catalytic efficiency of the engineered lipase was 39% higher than that of the wild type. The results demonstrate that rationally designed point mutations and disulfide bonds can effectively reduce the number of screened clones to enhance the thermostability of RML. lipase, whose structure is well established, can be widely applied in diverse chemical processes. Soluble expression of lipase in provides an opportunity to explore efficient methods for enhancing eukaryotic protein thermostability. This study highlights a strategy that combines computational algorithms to predict single point mutations and disulfide bonds in RML without losing catalytic activity. Through this strategy, an RML variant with greatly enhanced thermostability was obtained. This study provides a competitive alternative for wild-type RML in practical applications and further a rapid and effective strategy for thermostability engineering.

Most natural proteins exhibit poor thermostability, which limits their industrial application. Computer-aided rational design is an efficient purpose-oriented method that can improve protein thermostability. Numerous machine-learning-based methods have been designed to predict the changes in protein thermostability induced by mutations. However, all of these methods have certain limitations due to existing mutation coding methods that overlook protein sequence features. Here we propose a method to predict protein thermostability using convolutional neural networks based on an in-depth study of thermostability-related protein properties. This method comprises a three-dimensional coding algorithm, including protein mutation information and a strategy to extract neighboring features at protein mutation sites based on multiscale convolution. The accuracies on the S1615 and S388 data sets, which are widely used for protein thermostability predictions, reached 86.4 and 87%, respectively. The Matthews correlation coefficient was nearly double those produced using other methods. Furthermore, a model was constructed to predict the thermostability of Rhizomucor miehei lipase mutants based on the S3661 data set, a single amino acid mutation data set screened from the ProTherm protein thermodynamics database. Compared with the RIF strategy, which consists of three algorithms, i.e., Rosetta ddg monomer, I Mutant 3.0, and FoldX, the accuracy of the proposed method was higher (75.0 vs 66.7%), and the negative sample resolution was simultaneously enhanced. These results indicate that our prediction method more effectively assessed the protein thermostability and distinguished its features, making it a powerful tool to devise mutations that enhance the thermostability of proteins, particularly enzymes.

The thermostability of Candida rugosa lipase expressed in a eukaryotic host is enhanced with limited experimental effort based on rational design methods.