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.

High performance computational approach to study model describing reversible two-step enzymatic reaction with time fractional derivative.

Enzyme reactions have numerous applications in diverse disciplines of science like chemistry, biology and biomechanics. In this study, we examine the role and act of enzymes in chemical reactions which is considered in the frame of fractional order model. The proposed model includes system of four equations which are studied via Caputo fractional operator. The systems of non-linear equations are evaluated by a semi-analytical approach called q -homotopy analysis transform method. The uniqueness and existence of the solutions has been investigated through fixed point theorem. The solutions of the proposed model are achieved through the considered method and the obtained outcomes are in the form of series which shows rapid convergence. The solutions are computed and graphs are plotted for the obtained results using mathematica software. The achieved results by the proposed method are unique and illustrate the significant dynamics of the considered model via 3D plots and graphs. The results of this study demonstrate the importance and effectiveness of projected derivative and technique in the analysis of time dependent fractional mathematical models. This study also gives an idea to extend the applications of enzymatic reactions in drug development, bio mechanics, and chemical reactions in various cellular metabolisms. Also, enzymatic reactions have a vital role in the fields of the food industry for processing food, in biotechnology for the manufacture of biofuels, and in metabolic engineering to design metabolic pathways.

BayesianSSA: a Bayesian statistical model based on structural sensitivity analysis for predicting responses to enzyme perturbations in metabolic networks.

BACKGROUND: Chemical bioproduction has attracted attention as a key technology in a decarbonized society. In computational design for chemical bioproduction, it is necessary to predict changes in metabolic fluxes when up-/down-regulating enzymatic reactions, that is, responses of the system to enzyme perturbations. Structural sensitivity analysis (SSA) was previously developed as a method to predict qualitative responses to enzyme perturbations on the basis of the structural information of the reaction network. However, the network structural information can sometimes be insufficient to predict qualitative responses unambiguously, which is a practical issue in bioproduction applications. To address this, in this study, we propose BayesianSSA, a Bayesian statistical model based on SSA. BayesianSSA extracts environmental information from perturbation datasets collected in environments of interest and integrates it into SSA predictions. RESULTS: We applied BayesianSSA to synthetic and real datasets of the central metabolic pathway of Escherichia coli. Our result demonstrates that BayesianSSA can successfully integrate environmental information extracted from perturbation data into SSA predictions. In addition, the posterior distribution estimated by BayesianSSA can be associated with the known pathway reported to enhance succinate export flux in previous studies. CONCLUSIONS: We believe that BayesianSSA will accelerate the chemical bioproduction process and contribute to advancements in the field.

EnzChemRED, a rich enzyme chemistry relation extraction dataset.

Expert curation is essential to capture knowledge of enzyme functions from the scientific literature in FAIR open knowledgebases but cannot keep pace with the rate of new discoveries and new publications. In this work we present EnzChemRED, for Enzyme Chemistry Relation Extraction Dataset, a new training and benchmarking dataset to support the development of Natural Language Processing (NLP) methods such as (large) language models that can assist enzyme curation. EnzChemRED consists of 1,210 expert curated PubMed abstracts where enzymes and the chemical reactions they catalyze are annotated using identifiers from the protein knowledgebase UniProtKB and the chemical ontology ChEBI. We show that fine-tuning language models with EnzChemRED significantly boosts their ability to identify proteins and chemicals in text (86.30% F1 score) and to extract the chemical conversions (86.66% F1 score) and the enzymes that catalyze those conversions (83.79% F1 score). We apply our methods to abstracts at PubMed scale to create a draft map of enzyme functions in literature to guide curation efforts in UniProtKB and the reaction knowledgebase Rhea.

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.

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.

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.

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.

Recent advances in nanoenzymes for Alzheimer's disease treatment.

Alzheimer's disease (AD) remains one of the most challenging neurodegenerative disorders to treat, with oxidative stress playing a significant role in its pathology. Recent advancements in nanoenzymes technology offer a promising approach to mitigate this oxidative damage. Nanoenzymes, with their unique enzyme-mimicking activities, effectively scavenge reactive oxygen species and reduce oxidative stress, thereby providing neuroprotective effects. This review delves into the underlying mechanisms of AD, focusing on oxidative stress and its impact on disease progression. We explore the latest developments in nanoenzymes applications for AD treatment, highlighting their multifunctional capabilities and potential for targeted delivery to amyloid-beta plaques. Despite the exciting prospects, the clinical translation of nanoenzymes faces several challenges, including difficulties in brain targeting, consistent quality production, and ensuring safety and biocompatibility. We discuss these limitations in detail, emphasizing the need for rigorous evaluation and standardized protocols. This paper aims to provide a comprehensive overview of the current state of nanoenzymes research in AD, shedding light on both the opportunities and obstacles in the path towards effective clinical 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.

Designing intelligent bioorthogonal nanozymes: Recent advances of stimuli-responsive catalytic systems for biomedical applications.

Bioorthogonal nanozymes have emerged as a potent tool in biomedicine due to their unique ability to perform enzymatic reactions that do not interfere with native biochemical processes. The integration of stimuli-responsive mechanisms into these nanozymes has further expanded their potential, allowing for controlled activation and targeted delivery. As such, intelligent bioorthogonal nanozymes have received more and more attention in developing therapeutic approaches. This review provides a comprehensive overview of the recent advances in the development and application of stimuli-responsive bioorthogonal nanozymes. By summarizing the design outlines for anchoring bioorthogonal nanozymes with stimuli-responsive capability, this review seeks to offer valuable insights and guidance for the rational design of these remarkable materials. This review highlights the significant progress made in this exciting field with different types of stimuli and the various applications. Additionally, it also examines the current challenges and limitations in the design, synthesis, and application of these systems, and proposes potential solutions and research directions. This review aims to stimulate further research toward the development of more efficient and versatile stimuli-responsive bioorthogonal nanozymes for biomedical applications.

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.

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.

[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.

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.