Enzymatic reaction kinetics are central in analyzing enzymatic reaction mechanisms and target-enzyme optimization, and thus in biomanufacturing and other industries. The enzyme turnover number (k_cat) and Michaelis constant (K_m), key kinetic parameters for measuring enzyme catalytic efficiency, are crucial for analyzing enzymatic reaction mechanisms and the directed evolution of target enzymes. Experimental determination of k_cat and K_m is costly in terms of time, labor, and cost. To consider the intrinsic connection between k_cat and K_m and further improve the prediction performance_, we propose a universal pretrained multitask deep learning model, MPEK, to predict these parameters simultaneously while considering pH, temperature, and organismal information. Through testing on the same k_cat and K_m test datasets, MPEK demonstrated superior prediction performance over the previous models. Specifically, MPEK achieved the Pearson coefficient of 0.808 for predicting k_cat, improving ca. 14.6% and 7.6% compared to the DLKcat and UniKP models, and it achieved the Pearson coefficient of 0.777 for predicting K_m, improving ca. 34.9% and 53.3% compared to the Kroll_model and UniKP models. More importantly, MPEK was able to reveal enzyme promiscuity and was sensitive to slight changes in the mutant enzyme sequence. In addition, in three case studies, it was shown that MPEK has the potential for assisted enzyme mining and directed evolution. To facilitate in silico evaluation of enzyme catalytic efficiency, we have established a web server implementing this model, which can be accessed at http://mathtc.nscc-tj.cn/mpek.

In recent years, the widespread application of artificial intelligence algorithms in protein structure, function prediction, and de novo protein design has significantly accelerated the process of intelligent protein design and led to many noteworthy achievements. This advancement in protein intelligent design holds great potential to accelerate the development of new drugs, enhance the efficiency of biocatalysts, and even create entirely new biomaterials. Protein characterization is the key to the performance of intelligent protein design. However, there is no consensus on the most suitable characterization method for intelligent protein design tasks. This review describes the methods, characteristics, and representative applications of traditional descriptors, sequence-based and structure-based protein characterization. It discusses their advantages, disadvantages, and scope of application. It is hoped that this could help researchers to better understand the limitations and application scenarios of these methods, and provide valuable references for choosing appropriate protein characterization techniques for related research in the field, so as to better carry out protein research.

Tricyclene, a tricyclic monoterpene naturally occurring in plant essential oils, holds potential for the development of medicinal and fuel applications. In this study, we successfully synthesized tricyclene in E. coli by introducing the heterologous mevalonate (MVA) pathway along with Abies grandis geranyl diphosphate synthase (GPPS) and Nicotiana sylvestris tricyclene synthase (TS) XP_009791411. Initially, the shake-flask fermentation at 30 ^◦C yielded a tricyclene titer of 0.060 mg/L. By increasing the copy number of the TS-coding gene, we achieved a titer of 0.103 mg/L. To further enhance tricyclene production, optimal truncation in the N-terminal region of TS XP_009791411 resulted in an impressive highest titer of 47.671 mg/L, approximately a 794.5-fold improvement compared to its wild-type counterpart. To the best of our knowledge, this is the highest titer of the heterologous synthesis of tricyclene in E. coli. The SDS-PAGE analysis revealed that lowering induction temperature and truncating the random coil N-terminal region effectively improved TS solubility, which was closely associated with tricyclene production levels. Furthermore, by truncating other TSs, the titers of tricyclene were improved to different degrees.

Neo-allo-ocimene is a monoterpene which could be applied in pesticides, fragrances, and sustainable polymers. In this study, we mined a terpene synthase, AgTPS40, from the transcriptome of celery leaf tissues. Through sequence and phylogenetic analysis, AgTPS40 was characterized as a monoterpene synthase. The AgTPS40 gene was introduced into a heterologous mevalonate pathway hosted in Escherichia coli to enable terpene production. Gas chromatography–mass spectrometry analysis confirmed that AgTPS40 catalyzes the formation of neo-allo-ocimene, marking the first reported identification of a neo-allo-ocimene synthase. Subsequently, we optimized the fermentation conditions and achieved a yield of 933.35 mg/L in a 1 L shake flask, which represents the highest reported titer of neo-allo-ocimene to date. These results reveal the molecular basis of neo-allo-ocimene synthesis in celery and provide a sustainable way to obtain this compound.

Vibrio natriegens (V. natriegens) is an emerging synthetic biology chassis characterized by rapid growth, and its potential for the synthesis of sesquiterpenes (such as pentalenene) has not been developed. In this study, heterologous pentalenene biosynthesis was successfully established in V. natriegens via metabolic engineering. The optimization of gene dosage and culture conditions led to an increase in pentalenene yield from 0.75 mg/L to 39.4 mg/L, representing the highest titer reported in V. natriegens to date, though still markedly lower than yields achieved in conventional microbial hosts. Transcriptome analysis demonstrated that the exogenous mevalonate (MVA) pathway effectively activated terpenoid precursor synthesis, as evidenced by the up-regulation of key pathway genes. However, the endogenous methylerythritol 4-phosphate (MEP) pathway remained inactive, and genes involved in oxidative phosphorylation, the pentose phosphate pathway, and thiamine biosynthesis were down-regulated, leading to limited availability of ATP, NADPH, and acetyl-CoA. Competition for cofactors, particularly NADPH, further constrained precursor supply and pathway efficiency. This study confirmed the potential of V. natriegens as a pentalenene production platform and revealed its metabolic bottleneck, providing a theoretical basis for subsequent engineering optimization.