SpyTag is a peptide that can form an irreversible covalent linkage to its 12 kDa partner SpyCatcher via a spontaneous isopeptide bond. Herein, we fused SpyTag at the N-terminal of lichenase and SpyCatcher at C-terminal so that the termini of lichenase were locked together by the covalent interaction between the partners. In addition, an elastin-like polypeptides tag was subsequently attached to the C-terminus of SpyCatcher, thereby facilitating the non-chromatographic purification of cyclized lichenase. The study showed that the optimum temperature of the cyclized lichenase was about 5 °C higher in comparison to its linear counterpart. Moreover, nearly 80 % of the cyclized lichenase activities were retained after 100 °C exposure, whereas the linear form lost almost all of its activities. Therefore, the cyclized variant displayed a significantly higher thermal stability as temperature elevated and was resistant to hyperthermal denaturation. Besides, the Km value of the cyclized lichenase (7.58 ± 0.92 mg/mL) was approximately 1.7-fold lower than that of the linear one (12.96 ± 1.93 mg/mL), indicating a higher affinity with substrates. This new SpyTag/SpyCatcher cyclization strategy is deemed as a generalized reference for enhancing enzyme stability and can be effectively customized to the cyclization of various enzymes, hence a tremendous potential for successful application in the biocatalytic conversion of biomass to produce fuels and chemicals.

By proposing a improved Chous pseudo amino acid composition approach to extract the features of the sequences, a powerful predictor based on k-nearest neighbor was introduced to identify the types of lipases according to their sequences. To avoid redundancy and bias, demonstrations were performed on a dataset where none of the proteins has ≥ 25%sequence identity to any other. The overall success rate thus obtained by the 10-fold cross-validation test was over 90%, indicating that the improved Chous pseudo amino acid composition might be a useful tool for extracting the features of protein sequences, or at lease can play a complementary role to many of the other existing approaches.

Understanding of proteins adaptive to hypersaline environment and identifying them is a challenging task and would help to design stable proteins. Here, we have systematically analyzed the normalized amino acid compositions of 2121 halophilic and 2400 non-halophilic proteins. The results showed that halophilic protein contained more Asp at the expense of Lys, Ile, Cys and Met, fewer small and hydrophobic residues, and showed a large excess of acidic over basic amino acids. Then, we introduce a support vector machine method to discriminate the halophilic and non-halophilic proteins, by using a novel Pearson VII universal function based kernel. In the three validation check methods, it achieved an overall accuracy of 97.7%, 91.7% and 86.9% and outperformed other machine learning algorithms. We also address the influence of protein size on prediction accuracy and found the worse performance for small size proteins might be some significant residues (Cys and Lys) were missing in the proteins.