Strangers in a foreign land: 'Yeastizing' plant enzymes.

Expressing plant metabolic pathways in microbial platforms is an efficient, cost-effective solution for producing many desired plant compounds. As eukaryotic organisms, yeasts are often the preferred platform. However, expression of plant enzymes in a yeast frequently leads to failure because the enzymes are poorly adapted to the foreign yeast cellular environment. Here, we first summarize the current engineering approaches for optimizing performance of plant enzymes in yeast. A critical limitation of these approaches is that they are labour-intensive and must be customized for each individual enzyme, which significantly hinders the establishment of plant pathways in cellular factories. In response to this challenge, we propose the development of a cost-effective computational pipeline to redesign plant enzymes for better adaptation to the yeast cellular milieu. This proposition is underpinned by compelling evidence that plant and yeast enzymes exhibit distinct sequence features that are generalizable across enzyme families. Consequently, we introduce a data-driven machine learning framework designed to extract 'yeastizing' rules from natural protein sequence variations, which can be broadly applied to all enzymes. Additionally, we discuss the potential to integrate the machine learning model into a full design-build-test cycle.

MPEK: a multitask deep learning framework based on pretrained language models for enzymatic reaction kinetic parameters prediction.

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 (kcat) and Michaelis constant (Km), 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 kcat and Km is costly in terms of time, labor, and cost. To consider the intrinsic connection between kcat and Km 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 kcat and Km test datasets, MPEK demonstrated superior prediction performance over the previous models. Specifically, MPEK achieved the Pearson coefficient of 0.808 for predicting kcat, 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 Km, 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.

EC number prediction of protein sequences based on combination of hierarchical and global features.

The identification of enzyme functions plays a crucial role in understanding the mechanisms of biological activities and advancing the development of life sciences. However, existing enzyme EC number prediction methods did not fully utilize protein sequence information and still had shortcomings in identification accuracy. To address this issue, we proposed an EC number prediction network using hierarchical features and global features (ECPN-HFGF). This method first utilized residual networks to extract generic features from protein sequences, and then employed hierarchical feature extraction modules and global feature extraction modules to further extract hierarchical and global features of protein sequences. Subsequently, the prediction results of both feature types were combined, and a multitask learning framework was utilized to achieve accurate prediction of enzyme EC numbers. Experimental results indicated that the ECPN-HFGF method performed best in the task of predicting EC numbers for protein sequences, achieving macro F1 and micro F1 scores of 95.5% and 99.0%, respectively. The ECPN-HFGF method effectively combined hierarchical and global features of protein sequences, allowing for rapid and accurate EC number prediction. Compared to current commonly used methods, this method offers significantly higher prediction accuracy, providing an efficient approach for the advancement of enzymology research and enzyme engineering applications.

Emerging Nanomaterials as Versatile Nanozymes: A New Dimension in Biomedical Research.

The enzyme-mimicking nature of versatile nanomaterials proposes a new class of materials categorized as nano-enzymes, ornanozymes. They are artificial enzymes fabricated by functionalizing nanomaterials to generate active sites that can mimic enzyme-like functions. Materials extend from metals and oxides to inorganic nanoparticles possessing intrinsic enzyme-like properties. High cost, low stability, difficulty in separation, reusability, and storage issues of natural enzymes can be well addressed by nanozymes. Since 2007, more than 100 nanozymes have been reported that mimic enzymes like peroxidase, oxidase, catalase, protease, nuclease, hydrolase, superoxide dismutase, etc. In addition, several nanozymes can also exhibit multi-enzyme properties. Vast applications have been reported by exploiting the chemical, optical, and physiochemical properties offered by nanozymes. This review focuses on the reported nanozymes fabricated from a variety of materials along with their enzyme-mimicking activity involving tuning of materials such as metal nanoparticles (NPs), metal-oxide NPs, metal-organic framework (MOF), covalent organic framework (COF), and carbon-based NPs. Furthermore, diverse applications of nanozymes in biomedical research are discussed in detail.

Engineering Outer Membrane Vesicles to Carry Enzymes: Encapsulation, Isolation, Characterization, and Modification.

Outer membrane vesicles (OMVs) are small, spherical, nanoscale proteoliposomes released from Gram-negative bacteria that play an important role in cellular defense, pathogenesis, and signaling, among other functions. The functionality of OMVs can be enhanced by engineering developed for biomedical and biochemical applications. Here, we describe methods for directed packaging of enzymes into bacterial OMVs of E. coli using engineered molecular systems, such as localizing proteins to the inner or outer surface of the vesicle. Additionally, we detail some modification strategies for OMVs such as lyophilization and surfactant conjugation that enable the protection of activity of the packaged enzyme when exposed to non-physiological conditions such as elevated temperature, organic solvents, and repeated freeze/thaw that otherwise lead to a substantial loss in the activity of the free enzyme.

[Enzyme engineering in microbial biosynthesis of terpenoids: progress and perspectives].

Terpenoids, known for their structural and functional diversity, are highly valued, especially in food, cosmetics, and cleaning products. Microbial biosynthesis has emerged as a sustainable and environmentally friendly approach for the production of terpenoids. However, the natural enzymes involved in the synthesis of terpenoids have problems such as low activity, poor specificity, and insufficient stability, which limit the biosynthesis efficiency. Enzyme engineering plays a pivotal role in the microbial synthesis of terpenoids. By modifying the structures and functions of key enzymes, researchers have significantly improved the catalytic activity, specificity, and stability of enzymes related to terpenoid synthesis, providing strong support for the sustainable production of terpenoids. This article reviews the strategies for the modification of key enzymes in microbial synthesis of terpenoids, including improving enzyme activity and stability, changing specificity, and promoting mass transfer through multi-enzyme collaboration. Additionally, this article looks forward to the challenges and development directions of enzyme engineering in the microbial synthesis of terpenoids.

[Enzyme metabolism and functions in vitamin biosynthesis pathways].

Vitamins, as indispensable organic compounds in life activities, demonstrate a complex and refined metabolic network in organisms. This network involves the coordination of multiple enzymes and the integration of various metabolic pathways. Despite the achievements in metabolic engineering and catalytic mechanism research, the lack of studies regarding detailed enzymatic properties for a large number of key enzymes limits the enhancement of vitamin production efficiency and hinders the in-depth understanding and optimization of vitamin synthesis mechanisms. Such limitations not only restrict the industrial application of vitamins but also impede the development of related bio-technologies. This study comprehensively reviews the research progress in the enzymes involved in vitamin biosynthesis and details the current status of research on the enzymes of 13 vitamin synthesis pathways, including their catalytic mechanisms, kinetic properties, and applications in biology. In addition, this study compares the properties of enzymes involved in vitamin metabolic pathways and the glycolysis pathway, and reveals the characteristics of catalytic efficiency and substrate affinity of enzymes in vitamin synthesis pathways. Furthermore, this study discusses the potential and prospects of applying deep learning methods to the research on properties of enzymes associated with vitamin biosynthesis, giving new insights into the production and optimization of vitamins.

Computational studies on the catalytic potential of the double active site for enzyme engineering.

Proteins possessing double active sites have the potential to revolutionise enzyme design strategies. This study extensively explored an enzyme that contains both a natural active site (NAS) and an engineered active site (EAS), focusing on understanding its structural and functional properties. Metadynamics simulations were employed to investigate how substrates interacted with their respective active sites. The results revealed that both the NAS and EAS exhibited similar minimum energy states, indicating comparable binding affinities. However, it became apparent that the EAS had a weaker binding site for the substrate due to its smaller pocket and constrained conformation. Interestingly, the EAS also displayed dynamic behaviour, with the substrate observed to move outside the pocket, suggesting the possibility of substrate translocation. To gain further insights, steered molecular dynamics (SMD) simulations were conducted to study the conformational changes of the substrate and its interactions with catalytic residues. Notably, the substrate adopted distinct conformations, including near-attack conformations, in both the EAS and NAS. Nevertheless, the NAS demonstrated superior binding minima for the substrate compared to the EAS, reinforcing the observation that the engineered active site was less favourable for substrate binding due to its limitations. The QM/MM (Quantum mechanics and molecular mechanics) analyses highlight the energy disparity between NAS and EAS. Specifically, EAS exhibited elevated energy levels due to its engineered active site being located on the surface. This positioning exposes the substrate to solvents and water molecules, adding to the energy challenge. Consequently, the engineered enzyme did not provide a significant advantage in substrate binding over the single active site protein. Further, the investigation of internal channels and tunnels within the protein shed light on the pathways facilitating transport between the two active sites. By unravelling the complex dynamics and functional characteristics of this double-active site protein, this study offers valuable insights into novel strategies of enzyme engineering. These findings establish a solid foundation for future research endeavours aimed at harnessing the potential of double-active site proteins in diverse biotechnological applications.

Application of artificial backbone connectivity in the development of metalloenzyme mimics.

Metal-dependent enzymes are abundant and vital catalytic agents in nature. The functional versatility of metalloenzymes has made them common targets for improvement by protein engineering as well as mimicry by de novo designed sequences. In both strategies, the incorporation of non-canonical cofactors and/or non-canonical side chains has proved a useful tool. Less explored-but similarly powerful-is the utilization of non-canonical covalent modifications to the polypeptide backbone itself. Such efforts can entail either introduction of limited artificial monomers in natural chains to produce heterogeneous backbones or construction of completely abiotic oligomers that adopt defined folds. Herein, we review recent research applying artificial protein-like backbones in the construction of metalloenzyme mimics, highlighting progress as well as open questions in this emerging field.

DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D-structures.

Turnover numbers (kcat), which indicate an enzyme's catalytic efficiency, have a wide range of applications in fields including protein engineering and synthetic biology. Experimentally measuring the enzymes' kcat is always time-consuming. Recently, the prediction of kcat using deep learning models has mitigated this problem. However, the accuracy and robustness in kcat prediction still needs to be improved significantly, particularly when dealing with enzymes with low sequence similarity compared to those within the training dataset. Herein, we present DeepEnzyme, a cutting-edge deep learning model that combines the most recent Transformer and Graph Convolutional Network (GCN) to capture the information of both the sequence and 3D-structure of a protein. To improve the prediction accuracy, DeepEnzyme was trained by leveraging the integrated features from both sequences and 3D-structures. Consequently, DeepEnzyme exhibits remarkable robustness when processing enzymes with low sequence similarity compared to those in the training dataset by utilizing additional features from high-quality protein 3D-structures. DeepEnzyme also makes it possible to evaluate how point mutations affect the catalytic activity of the enzyme, which helps identify residue sites that are crucial for the catalytic function. In summary, DeepEnzyme represents a pioneering effort in predicting enzymes' kcat values with improved accuracy and robustness compared to previous algorithms. This advancement will significantly contribute to our comprehension of enzyme function and its evolutionary patterns across species.

Pen and palm model to envision the coexistence of induced-fit and substrate-strain theories of enzyme action.

Competency-based physiology and biochemistry education can benefit from the creative integration of imaginative narratives into traditional teaching methods. This paper proposes an innovative model using a pen and palm analogy to visualize enzyme function theories. The pen (substrate) must fit snugly into the palm (enzyme's active site) for catalysis to occur, akin to induced-fit theory. Pressing the pen's top button with the thumb represents the strain needed to convert substrate (pen with nib inside) into product (pen with nub out, ready to write). By leveraging everyday objects creatively, students can enhance their understanding and engagement with enzymatic reactions.NEW & NOTEWORTHY Understanding how enzymes work can be tricky, but a new teaching method using everyday objects like pens and palms helps make it easier. Two main theories explain this: the induced-fit model and the substrate-strain model. To visualize this, imagine a pen as the substrate and your palm as the enzyme. When you hold the pen with your fingers (induced-fit), it's like the enzyme changing shape to hold the substrate. Pressing the pen's button with your thumb (substrate-strain) is like the enzyme applying pressure to make the pen ready to write. This simple analogy helps students better understand these complex processes, making learning more engaging and accessible.

Advancements in Aqueous Two-Phase Systems for Enzyme Extraction, Purification, and Biotransformation.

In recent years, the increasing need for energy conservation and environmental protection has driven industries to explore more efficient and sustainable processes. Liquid-liquid extraction (LLE) is a common method used in various sectors for separating components of liquid mixtures. However, the traditional use of toxic solvents poses significant health and environmental risks, prompting the shift toward green solvents. This review deals with the principles, applications, and advantages of aqueous two-phase systems (ATPS) as an alternative to conventional LLE. ATPS, which typically utilize water and nontoxic components, offer significant benefits such as high purity and single-step biomolecule extraction. This paper explores the thermodynamic principles of ATPS, factors influencing enzyme partitioning, and recent advancements in the field. Specific emphasis is placed on the use of ATPS for enzyme extraction, showcasing its potential in improving yields and purity while minimizing environmental impact. The review also highlights the role of ionic liquids and deep eutectic solvents in enhancing the efficiency of ATPS, making them viable for industrial applications. The discussion extends to the challenges of integrating ATPS into biotransformation processes, including enzyme stability and process optimization. Through comprehensive analysis, this paper aims to provide insights into the future prospects of ATPS in sustainable industrial practices and biotechnological applications.

Multi-modal deep learning enables efficient and accurate annotation of enzymatic active sites.

Annotating active sites in enzymes is crucial for advancing multiple fields including drug discovery, disease research, enzyme engineering, and synthetic biology. Despite the development of numerous automated annotation algorithms, a significant trade-off between speed and accuracy limits their large-scale practical applications. We introduce EasIFA, an enzyme active site annotation algorithm that fuses latent enzyme representations from the Protein Language Model and 3D structural encoder, and then aligns protein-level information with the knowledge of enzymatic reactions using a multi-modal cross-attention framework. EasIFA outperforms BLASTp with a 10-fold speed increase and improved recall, precision, f1 score, and MCC by 7.57%, 13.08%, 9.68%, and 0.1012, respectively. It also surpasses empirical-rule-based algorithm and other state-of-the-art deep learning annotation method based on PSSM features, achieving a speed increase ranging from 650 to 1400 times while enhancing annotation quality. This makes EasIFA a suitable replacement for conventional tools in both industrial and academic settings. EasIFA can also effectively transfer knowledge gained from coarsely annotated enzyme databases to smaller, high-precision datasets, highlighting its ability to model sparse and high-quality databases. Additionally, EasIFA shows potential as a catalytic site monitoring tool for designing enzymes with desired functions beyond their natural distribution.

Analysis of Correlation Effects of Double Mutations in Enzymes: A Revised Residual-Contact Network Clique Model.

The relationship between amino acid mutations and enzyme bioactivity is a significant challenge in modern bio-industrial applications. Despite many successful designs relying on complex correlations among mutations at different enzyme sites, the underlying mechanisms of these correlations still need to be explored. In this study, we introduced a revised version of the residual-contact network clique model to investigate the additive effect of double mutations based on the mutation occurrence topology, secondary structures, and physicochemical properties. The model was applied to a set of 182 double mutations reported in three extensively studied enzymes, and it successfully identified over 90% of additive double mutations and a majority of non-additive double mutations. The calculations revealed that the mutation additivity depends intensely on the studied mutation sites' topology and physicochemical properties. For example, double mutations on irregular secondary structure regions tend to be non-additive. Our method provides valuable tools for facilitating enzyme design and optimization. The code and relevant data are available at Github.

Recent advances in the design and optimization of artificial metalloenzymes.

Embedding a catalytically competent transition metal into a protein scaffold affords an artificial metalloenzyme (ArM). Such hybrid catalysts display features that are reminiscent of both homogeneous and enzymatic catalysts. Pioneered by Whitesides and Kaiser in the late 1970s, this field of ArMs has expanded over the past two decades, marked by ever-increasing diversity in reaction types, cofactors, and protein scaffolds. Recent noteworthy developments include i) the use of earth-abundant metal cofactors, ii) concurrent cascade reactions, iii) synergistic catalysis, and iv) in vivo catalysis. Thanks to significant progress in computational protein design, ArMs based on de novo-designed proteins and tailored chimeric proteins promise a bright future for this exciting field.

Nanozymes for biomedical applications: Multi-metallic systems may improve activity but at the cost of higher toxicity?

Nanozymes are nanomaterials with intrinsic enzyme-like activity with selected advantages over native enzymes such as simple synthesis, controllable activity, high stability, and low cost. These materials have been explored as surrogates to natural enzymes in biosensing, therapeutics, environmental protection, and many other fields. Among different nanozymes classes, metal- and metal oxide-based nanozymes are the most widely studied. In recent years, bi- and tri-metallic nanomaterials have emerged often showing improved nanozyme activity, some of which even possess multifunctional enzyme-like activity. Taking this concept even further, high-entropy nanomaterials, that is, complex multicomponent alloys and ceramics like oxides, may potentially enhance activity even further. However, the addition of various elements to increase catalytic activity may come at the cost of increased toxicity. Since many nanozyme compositions are currently being explored for in vivo biomedical applications, such as cancer therapeutics, toxicity considerations in relation to nanozyme application in biomedicine are of vital importance for translation. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials Diagnostic Tools > Diagnostic Nanodevices.

Theory on the rate equations of Michaelis-Menten type enzyme kinetics with competitive inhibition.

We derive approximate expressions for pre- and post-steady state regimes of the velocity-substrate-inhibitor spaces of the Michaelis-Menten enzyme kinetic scheme with fully and partial competitive inhibition. Our refinement over the currently available standard quasi steady state approximation (sQSSA) seems to be valid over wide range of enzyme to substrate and enzyme to inhibitor concentration ratios. Further, we show that the enzyme-inhibitor-substrate system can exhibit temporally well-separated two different steady states with respect to both enzyme-substrate and enzyme-inhibitor complexes under certain conditions. We define the ratios fS = vmax/(KMS + e0) and fI = umax/(KMI + e0) as the acceleration factors with respect to the catalytic conversion of substrate and inhibitor into their respective products. Here KMS and KMI are the Michaelis-Menten parameters associated respectively with the binding of substrate and inhibitor with the enzyme, vmax and umax are the respective maximum reaction velocities and e0, s0, and i0 are total enzyme, substrate and inhibitor levels. When (fS/fI) < 1, then enzyme-substrate complex will show multiple steady states and it reaches the full-fledged steady state only after the depletion of enzyme-inhibitor complex. When (fS/fI) > 1, then the enzyme-inhibitor complex will show multiple steady states and it reaches the full-fledged steady state only after the depletion of enzyme-substrate complex. This multi steady-state behavior especially when (fS/fI) ≠ 1 is the root cause of large amount of error in the estimation of various kinetic parameters of fully and partial competitive inhibition schemes using sQSSA. Remarkably, we show that our refined expressions for the reaction velocities over enzyme-substrate-inhibitor space can control this error more significantly than the currently available sQSSA expressions.

Physical limits to acceleration of enzymatic reactions inside phase-separated compartments.

We present a theoretical analysis of phase-separated compartments to facilitate enzymatic chemical reactions. While phase separation can facilitate reactions by increasing local concentration, it can also hinder the mobility of reactants. In particular, we find that the attractive interactions that concentrate reactants within the dense phase can inhibit reactions by lowering the mobility of the reactants. This mobility loss severely limits the potential to enhance reaction rates. Phase separation provides greater benefit in situations where multiple sequential reactions occur and/or high order reactions, provided the enzymes are unsaturated, transport to the condensate is not limiting, and the reactants are mobile. We show that mobility can be maintained if recruitment to the condensed phase is driven by multiple attractive moieties that can bind and release independently. However, the spacers necessary to ensure independence between stickers are prone to entangle with the dense phase scaffold. We find an optimal sticker affinity that balances the need for rapid binding/unbinding kinetics and minimal entanglement. Reaction rates can be accelerated by shrinking the size of the dense phase with a corresponding increase in the number of stickers. Our results showcase the potential capabilities of phase-separated compartments to act as biochemical reaction crucibles within living cells.

The mechanistic insights into different aspects of promiscuity in metalloenzymes.

Enzymes are nature's ultimate machinery to catalyze complex reactions. Though enzymes are evolved to catalyze specific reactions, they also show significant promiscuity in reactions and substrate selection. Metalloenzymes contain a metal ion or metal cofactor in their active site, which is crucial in their catalytic activity. Depending on the metal and its coordination environment, the metal ion or cofactor may function as a Lewis acid or base and a redox center and thus can catalyze a plethora of natural reactions. In fact, the versatility in the oxidation state of the metal ions provides metalloenzymes with a high level of catalytic adaptability and promiscuity. In this chapter, we discuss different aspects of promiscuity in metalloenzymes by using several recent experimental and theoretical works as case studies. We start our discussion by introducing the concept of promiscuity and then we delve into the mechanistic insight into promiscuity at the molecular level.