Data-driven strategies for the computational design of enzyme thermal stability: trends, perspectives, and prospects.

Thermal stability is one of the most important properties of enzymes, which sustains life and determines the potential for the industrial application of biocatalysts. Although traditional methods such as directed evolution and classical rational design contribute greatly to this field, the enormous sequence space of proteins implies costly and arduous experiments. The development of enzyme engineering focuses on automated and efficient strategies because of the breakthrough of high-throughput DNA sequencing and machine learning models. In this review, we propose a data-driven architecture for enzyme thermostability engineering and summarize some widely adopted datasets, as well as machine learning-driven approaches for designing the thermal stability of enzymes. In addition, we present a series of existing challenges while applying machine learning in enzyme thermostability design, such as the data dilemma, model training, and use of the proposed models. Additionally, a few promising directions for enhancing the performance of the models are discussed. We anticipate that the efficient incorporation of machine learning can provide more insights and solutions for the design of enzyme thermostability in the coming years.

A sequence embedding method for enzyme optimal condition analysis.

BACKGROUND: An enzyme activity is influenced by the external environment. It is important to have an enzyme remain high activity in a specific condition. A usual way is to first determine the optimal condition of an enzyme by either the gradient test or by tertiary structure, and then to use protein engineering to mutate a wild type enzyme for a higher activity in an expected condition. RESULTS: In this paper, we investigate the optimal condition of an enzyme by directly analyzing the sequence. We propose an embedding method to represent the amino acids and the structural information as vectors in the latent space. These vectors contain information about the correlations between amino acids and sites in the aligned amino acid sequences, as well as the correlation with the optimal condition. We crawled and processed the amino acid sequences in the glycoside hydrolase GH11 family, and got 125 amino acid sequences with optimal pH condition. We used probabilistic approximation method to implement the embedding learning method on these samples. Based on these embedding vectors, we design a computational score to determine which one has a better optimal condition for two given amino acid sequences and achieves the accuracy 80% on the test proteins in the same family. We also give the mutation suggestion such that it has a higher activity in an expected environment, which is consistent with the previously professional wet experiments and analysis. CONCLUSION: A new computational method is proposed for the sequence based on the enzyme optimal condition analysis. Compared with the traditional process that involves a lot of wet experiments and requires multiple mutations, this method can give recommendations on the direction and location of amino acid substitution with reference significance for an expected condition in an efficient and effective way.

Multi-omics study of sulfur metabolism affecting functional microbial community succession during aerobic solid-state fermentation.

Microbial-mediated sulfur metabolism is closely related to carbon and nitrogen metabolism in natural biological systems. In this study, the effects of sulfur metabolism on microbial communities and functional enzyme succession were investigated based on integrated multi-omics by adding sulfur-containing compounds to aerobic fermentation systems. Sulfur powder was oxidized to S2O32- and subsequently to SO42- by the microbial sulfur-oxidizing system, which lowered the pH to 7.5 on day 7. The decrease in pH resulted in Planifilum (secreted S8, M17 and M32 proteases) losing its competitive advantage, whereas Novibacillus (secreted M14 and M19 metalloproteases) became dominant. Structural proteomics indicated that the surface of Novibacillus proteases has more negatively charged amino acid residues that help maintain protein stability at low pH. These findings aid understanding of the effects of sulfur metabolism on fermentation and the mechanism of microbial adaptation after pH reduction, providing new perspectives on the optimization of fermentation processes.

High mass loading and additive-free prussian blue analogue based flexible electrodes for Na-ion supercapacitors.

Supercapacitor electrodes often suffer from the low mass loading of active substances and the unsatisfactory ion/charge transport features due to the use of various additives. Exploring high mass loading and additive-free electrodes is of huge significance to develop advanced supercapacitors with commercial application prospects, which still remains challenging. Herein, high mass loading CoFe-prussian blue analogue (CoFe-PBA) electrodes are developed by a facile co-precipitation method using activated carbon cloth (ACC) as the flexible substrate. The homogeneous nanocube structure, large specific surface area (143.9 m2 g-1) and appropriate pore size distribution (3.4 nm) of the CoFe-PBA endow the as-prepared CoFe-PBA/ACC electrodes with low resistance and appealing ion diffusion characteristics. Typically, the high areal capacitance (1155.0 mF cm-2 at 0.5 mA cm-2) is obtained for high mass loading CoFe-PBA/ACC electrodes (9.7 mg cm-2). Furthermore, symmetrical flexible supercapacitors (FSCs) are constructed using CoFe-PBA/ACC electrodes and Na2SO4/polyving alcohol (Na2SO4/PVA) gel electrolyte, achieving superior stability (85.6% capacitance retention after 5,000 cycles), maximum energy density of 33.8 µWh cm-2 at 200.0 µW cm-2 and promising mechanical flexibility. This work is expected to offer inspirations for the development of high mass loading and additive-free electrodes for FSCs.