The BRENDA enzyme information system–From a database to an expert system

Harnessing the Energy Potential and Value-Added Products from the Treatment of Sugarcane Vinasse: Maximizing Methane Production Through Co-Digestion with Sugarcane Molasses and Enhanced Organic Loading

This study assessed the impact of organic loading rate (OLR) on methane (CH4) production in the anaerobic co-digestion (AcoD) of sugarcane vinasse and molasses (SVM) (1:1 ratio) within a thermophilic fluidized bed reactor (AFBR). The OLR ranged from 5 to 27.5 kg COD.m-3.d-1, with a fixed hydraulic retention time (HRT) of 24 h. Organic matter removal varied from 56 to 84%, peaking at an OLR of 5 kg COD.m-3.d-1. Maximum CH4 yield (MY) (272.6 mL CH4.g-1CODrem) occurred at an OLR of 7.5 kg COD.m-3.d-1, while the highest CH4 production rate (MPR) (4.0 L CH4.L-1.d-1) and energy potential (E.P.) (250.5 kJ.d-1) were observed at an OLR of 20 kg COD.m-3.d-1. The AFBR exhibited stability across all OLR. At 22.5 kg COD.m-3.d-1, a decrease in MY indicated methanogenesis imbalance and inhibitory organic compound accumulation. OLR influenced microbial populations, with Firmicutes and Thermotogota constituting 43.9% at 7.5 kg COD.m-3.d-1, and Firmicutes dominating (52.7%) at 27.5 kg COD.m-3.d-1. Methanosarcina (38.9%) and hydrogenotrophic Methanothermobacter (37.6%) were the prevalent archaea at 7.5 kg COD.m-3.d-1 and 27.5 kg COD.m-3.d-1, respectively. Therefore, this study demonstrates that the organic loading rate significantly influences the efficiency of methane production and the stability of microbial communities during the anaerobic co-digestion of sugarcane vinasse and molasses, indicating that optimized conditions can maximize energy yield and maintain methanogenic balance.

Insights on mining the pangenome of Sphingobacterium thalpophilum NMS02 S296 from the resistant banana cultivar Pisang lilin confirms the antifungal action against Fusarium oxysporum f. sp. cubense

Introduction

Fusarium wilt, caused by Fusarium oxysporum f. sp. cubense (Foc), poses a significant global threat to banana cultivation. Conventional methods of disease management are increasingly challenged, thus making it necessary to explore alternative strategies. Bacterial endophytes, particularly from resistant genotypes, are gaining attention as potential biocontrol agents. Sphingobacterium thalpophilum, isolated from the resistant banana cultivar Pisang lilin (JALHSB010000001-JALHSB010000029), presents an intriguing prospect for combating Fusarium wilt. However, its underlying biocontrol mechanisms remain poorly understood. This study aimed to elucidate the antifungal efficacy of S. thalpophilum NMS02 S296 against Foc and explore its biocontrol mechanisms at the genomic level.

Methods

Whole genome sequencing of S. thalpophilum NMS02 S296 was conducted using next-generation sequencing technologies and bioinformatics analyses were performed to identify genes associated with antifungal properties. In vitro assays were used to assess the inhibitory effects of the bacterial isolate on the mycelial growth of Foc. To explore the biomolecules responsible for the observed antagonistic activity, metabolites diffused into the agar at the zone of inhibition between Foc S16 and S. thalpophilum NMS02 S296 were extracted and identified.

Results

Whole genome sequencing revealed an array of genes encoding antifungal enzymes and secondary metabolites in S. thalpophilum NMS02 S296. In vitro experiments demonstrated significant inhibition of Foc mycelial growth by the bacterial endophyte. Comparative genomic analysis highlighted unique genomic features in S. thalpophilum linked to its biocontrol potential, setting it apart from other bacterial species.

Discussion

The study underscores the remarkable antifungal efficacy of S. thalpophilum NMS02 S296 against Fusarium wilt. The genetic basis for its biocontrol potential was elucidated through whole genome sequencing, shedding light on the mechanisms behind its antifungal activity. This study advanced our understanding of bacterial endophytes as biocontrol agents and offers a promising avenue for plant growth promotion towards sustainable strategies to mitigate Fusarium wilt in banana cultivation.

Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures

Enzymes are crucial in numerous biological processes, with the Enzyme Commission (EC) number being a commonly used method for defining enzyme function. However, current EC number prediction technologies have not fully recognized the importance of enzyme active sites and structural characteristics. Here, we propose GraphEC, a geometric graph learning-based EC number predictor using the ESMFold-predicted structures and a pre-trained protein language model. Specifically, we first construct a model to predict the enzyme active sites, which is utilized to predict the EC number. The prediction is further improved through a label diffusion algorithm by incorporating homology information. In parallel, the optimum pH of enzymes is predicted to reflect the enzyme-catalyzed reactions. Experiments demonstrate the superior performance of our model in predicting active sites, EC numbers, and optimum pH compared to other state-of-the-art methods. Additional analysis reveals that GraphEC is capable of extracting functional information from protein structures, emphasizing the effectiveness of geometric graph learning. This technology can be used to identify unannotated enzyme functions, as well as to predict their active sites and optimum pH, with the potential to advance research in synthetic biology, genomics, and other fields.

The proteomic landscape of fall armyworm oral secretion reveals its role in plant adaptation

BACKGROUND

The fall armyworm (FAW, Spodoptera frugiperda (J.E. Smith)) is a polyphagous agricultural pest with rapidly evolving adaptations to host plants. We found the oral secretion (OS) of FAW from different plants influences plant defense response differentially, suggesting its role in adapting to host plants. However, the protein expression profile of FAW OS respond to different plants is largely unknown.

RESULTS

Here, from the mass spectrometry assay, we identified a total of 256 proteins in the OS of FAW fed on cotton (Gossypium hirsutum L.), tobacco (Nicotiana benthamiana Domin), maize (Zea mays L.) and artificial diet. The FAW OS primarily comprise of 60 proteases, 32 esterases and 92 non-enzymatic proteins. It displays high plasticity across different diets. We found that more than half of the esterases are lipases which have been reported as insect elicitors to enhance plant defense response. The lipase accumulation in cotton-fed larvae was the highest, followed by maize-fed larvae. In the presence of lipase inhibitors, the enhanced induction on defense genes in wounded leaves by OS was attenuated. However, the putative effectors were most highly accumulated in the OS from FAW larvae fed on maize compared to those fed on other diets. We identified that one of them (VRLP4) reduces the OS-mediated induction on defense genes in wounded leaves.

CONCLUSION

Together, our investigation presents the proteomic landscape of the OS of FAW influenced by different diets and reveals diet-mediated plasticity of OS is involved in FAW adaptation to host plants. © 2024 Society of Chemical Industry.

Optimization of Interfacial Properties Improved the Stability and Activity of the Catalase Enzyme Immobilized on Plastic Nanobeads

The immobilization of catalase (CAT), a crucial oxidoreductase enzyme involved in quenching reactive oxygen species, on colloids and nanoparticles presents a promising strategy to improve dispersion and storage stability while maintaining its activity. Here, the immobilization of CAT onto polymeric nanoparticles (positively (AL) or negatively (SL) charged) was implemented directly (AL) or via surface functionalization (SL) with water-soluble chitosan derivatives (glycol chitosan (GC) and methyl glycol chitosan (MGC)). The interfacial properties were optimized to obtain highly stable AL-CAT, SL-GC-CAT, and SL-MGC-CAT dispersions, and confocal microscopy confirmed the presence of CAT in the composites. Assessment of hydrogen peroxide decomposition ability revealed that applying chitosan derivatives in the immobilization process not only enhanced colloidal stability but also augmented the activity and reusability of CAT. In particular, the use of MGC has led to significant advances, indicating its potential for industrial and biomedical applications. Overall, the findings highlight the advantages of using chitosan derivatives in CAT immobilization processes to maintain the stability and activity of the enzyme as well as provide important data for the development of processable enzyme-based nanoparticle systems to combat reactive oxygen species.

In silico approaches to study the human asparagine synthetase: An insight of the interaction between the enzyme active sites and its substrates

Cancer is a leading concern and important cause of death worldwide. Cancer is a non-communicable illness defined as uncontrolled division of cells. It can develop into metastatic cancer when tumor cells migrate to other organs. In recent years evidence has emerged that the bioavailability of Asn play a crucial role in cancer metastasis. Asn is a non-essential amino acid formed from an ATP dependent catalyzed reaction by the enzyme asparagine synthetase (ASNS), where Asp and Gln are converted to Asn and Glu, respectively. The human ASNS enzyme consist of 561 amino acids, with a molecular weight of 64 KDa. ASNS governs the activation of transcriptional factors that regulate the process of metastasis. In this work the 3D model of ASNS in E. coli (AS-B) and the human ASNS docked with its different ligands have been used to study the 3D mechanism of the conversion of Asp and Gln to Asn and Glu, in human ASNS. The stability evaluation of the docked complexes was checked by molecular dynamic simulation through the bioinformatic tool Desmond. The binding residues and their interactions can be exploited for the development of inhibitors, as well as for finding new drug molecules against ASNS and prevention of metastatic cancer.

Two-domain GH30 xylanase from human gut microbiota as a tool for enzymatic production of xylooligosaccharides: Crystallographic structure and a synergy with GH11 xylosidase

Production of value-added compounds and sustainable materials from agro-industrial residues is essential for better waste management and building of circular economy. This includes valorization of hemicellulosic fraction of plant biomass, the second most abundant biopolymer from plant cell walls, aiming to produce prebiotic oligosaccharides, widely explored in food and feed industries. In this work, we conducted biochemical and biophysical characterization of a prokaryotic two-domain R. champanellensis xylanase from glycoside hydrolase (GH) family 30 (RcXyn30A), and evaluated its applicability for XOS production from glucuronoxylan in combination with two endo-xylanases from GH10 and GH11 families and a GH11 xylobiohydrolase. RcXyn30A liberates mainly long monoglucuronylated xylooligosaccharides and is inefficient in cleaving unbranched oligosaccharides. Crystallographic structure of RcXyn30A catalytic domain was solved and refined to 1.37 Å resolution. Structural analysis of the catalytic domain releveled that its high affinity for glucuronic acid substituted xylan is due to the coordination of the substrate decoration by several hydrogen bonds and ionic interactions in the subsite -2. Furthermore, the protein has a larger β5-α5 loop as compared to other GH30 xylanases, which might be crucial for creating an additional aglycone subsite (+3) of the catalytic site. Finally, RcXyn30A activity is synergic to that of GH11 xylobiohydrolase.

Expanding chemistry through in vitro and in vivo biocatalysis

Living systems contain a vast network of metabolic reactions, providing a wealth of enzymes and cells as potential biocatalysts for chemical processes. The properties of protein and cell biocatalysts-high selectivity, the ability to control reaction sequence and operation in environmentally benign conditions-offer approaches to produce molecules at high efficiency while lowering the cost and environmental impact of industrial chemistry. Furthermore, biocatalysis offers the opportunity to generate chemical structures and functions that may be inaccessible to chemical synthesis. Here we consider developments in enzymes, biosynthetic pathways and cellular engineering that enable their use in catalysis for new chemistry and beyond.

From transcriptomics to digital twins of organ function

Cell level functions underlie tissue and organ physiology. Gene expression patterns offer extensive views of the pathways and processes within and between cells. Single cell transcriptomics provides detailed information on gene expression within cells, cell types, subtypes and their relative proportions in organs. Functional pathways can be scalably connected to physiological functions at the cell and organ levels. Integrating experimentally obtained gene expression patterns with prior knowledge of pathway interactions enables identification of networks underlying whole cell functions such as growth, contractility, and secretion. These pathways can be computationally modeled using differential equations to simulate cell and organ physiological dynamics regulated by gene expression changes. Such computational systems can be thought of as parts of digital twins of organs. Digital twins, at the core, need computational models that represent in detail and simulate how dynamics of pathways and networks give rise to whole cell level physiological functions. Integration of transcriptomic responses and numerical simulations could simulate and predict whole cell functional outputs from transcriptomic data. We developed a computational pipeline that integrates gene expression timelines and systems of coupled differential equations to generate cell-type selective dynamical models. We tested our integrative algorithm on the eicosanoid biosynthesis network in macrophages. Converting transcriptomic changes to a dynamical model allowed us to predict dynamics of prostaglandin and thromboxane synthesis and secretion by macrophages that matched published lipidomics data obtained in the same experiments. Integration of cell-level system biology simulations with genomic and clinical data using a knowledge graph framework will allow us to create explicit predictive models that mechanistically link genomic determinants to organ function. Such integration requires a multi-domain ontological framework to connect genomic determinants to gene expression and cell pathways and functions to organ level phenotypes in healthy and diseased states. These integrated scalable models of tissues and organs as accurate digital twins predict health and disease states for precision medicine.

Vocabulary Matters: An Annotation Pipeline and Four Deep Learning Algorithms for Enzyme Named Entity Recognition

Enzymes are indispensable in many biological processes, and with biomedical literature growing exponentially, effective literature review becomes increasingly challenging. Natural language processing methods offer solutions to streamline this process. This study aims to develop an annotated enzyme corpus for training and evaluating enzyme named entity recognition (NER) models. A novel pipeline, combining dictionary matching and rule-based keyword searching, automatically annotated enzyme entities in >4800 full-text publications. Four deep learning NER models were created with different vocabularies (BioBERT/SciBERT) and architectures (BiLSTM/transformer) and evaluated on 526 manually annotated full-text publications. The annotation pipeline achieved an F1-score of 0.86 (precision = 1.00, recall = 0.76), surpassed by fine-tuned transformers for F1-score (BioBERT: 0.89, SciBERT: 0.88) and recall (0.86) with BiLSTM models having higher precision (0.94) than transformers (0.92). The annotation pipeline runs in seconds on standard laptops with almost perfect precision, but was outperformed by fine-tuned transformers in terms of F1-score and recall, demonstrating generalizability beyond the training data. In comparison, SciBERT-based models exhibited higher precision, and BioBERT-based models exhibited higher recall, highlighting the importance of vocabulary and architecture. These models, representing the first enzyme NER algorithms, enable more effective enzyme text mining and information extraction. Codes for automated annotation and model generation are available from https://github.com/omicsNLP/enzymeNER and https://zenodo.org/doi/10.5281/zenodo.10581586.

Enhancing Machine-Learning Prediction of Enzyme Catalytic Temperature Optima through Amino Acid Conservation Analysis

Enzymes play a crucial role in various industrial production and pharmaceutical developments, serving as catalysts for numerous biochemical reactions. Determining the optimal catalytic temperature (Topt) of enzymes is crucial for optimizing reaction conditions, enhancing catalytic efficiency, and accelerating the industrial processes. However, due to the limited availability of experimentally determined Topt data and the insufficient accuracy of existing computational methods in predicting Topt, there is an urgent need for a computational approach to predict the Topt values of enzymes accurately. In this study, using phosphatase (EC 3.1.3.X) as an example, we constructed a machine learning model utilizing amino acid frequency and protein molecular weight information as features and employing the K-nearest neighbors regression algorithm to predict the Topt of enzymes. Usually, when conducting engineering for enzyme thermostability, researchers tend not to modify conserved amino acids. Therefore, we utilized this machine learning model to predict the Topt of phosphatase sequences after removing conserved amino acids. We found that the predictive model's mean coefficient of determination (R2) value increased from 0.599 to 0.755 compared to the model based on the complete sequences. Subsequently, experimental validation on 10 phosphatase enzymes with undetermined optimal catalytic temperatures shows that the predicted values of most phosphatase enzymes based on the sequence without conservative amino acids are closer to the experimental optimal catalytic temperature values. This study lays the foundation for the rapid selection of enzymes suitable for industrial conditions.

Improving the enzymatic activity and stability of N-carbamoyl hydrolase using deep learning approach

BACKGROUND

Optically active D-amino acids are widely used as intermediates in the synthesis of antibiotics, insecticides, and peptide hormones. Currently, the two-enzyme cascade reaction is the most efficient way to produce D-amino acids using enzymes DHdt and DCase, but DCase is susceptible to heat inactivation. Here, to enhance the enzymatic activity and thermal stability of DCase, a rational design software "Feitian" was developed based on kcat prediction using the deep learning approach.

RESULTS

According to empirical design and prediction of "Feitian" software, six single-point mutants with high kcat value were selected and successfully constructed by site-directed mutagenesis. Out of six, three mutants (Q4C, T212S, and A302C) showed higher enzymatic activity than the wild-type. Furthermore, the combined triple-point mutant DCase-M3 (Q4C/T212S/A302C) exhibited a 4.25-fold increase in activity (29.77 ± 4.52 U) and a 2.25-fold increase in thermal stability as compared to the wild-type, respectively. Through the whole-cell reaction, the high titer of D-HPG (2.57 ± 0.43 mM) was produced by the mutant Q4C/T212S/A302C, which was about 2.04-fold of the wild-type. Molecular dynamics simulation results showed that DCase-M3 significantly enhances the rigidity of the catalytic site and thus increases the activity of DCase-M3.

CONCLUSIONS

In this study, an efficient rational design software "Feitian" was successfully developed with a prediction accuracy of about 50% in enzymatic activity. A triple-point mutant DCase-M3 (Q4C/T212S/A302C) with enhanced enzymatic activity and thermostability was successfully obtained, which could be applied to the development of a fully enzymatic process for the industrial production of D-HPG.

ThermoFinder: A sequence-based thermophilic proteins prediction framework

Thermophilic proteins are important for academic research and industrial processes, and various computational methods have been developed to identify and screen them. However, their performance has been limited due to the lack of high-quality labeled data and efficient models for representing protein. Here, we proposed a novel sequence-based thermophilic proteins prediction framework, called ThermoFinder. The results demonstrated that ThermoFinder outperforms previous state-of-the-art tools on two benchmark datasets, and feature ablation experiments confirmed the effectiveness of our approach. Additionally, ThermoFinder exhibited exceptional performance and consistency across two newly constructed datasets, one of these was specifically constructed for the regression-based prediction of temperature optimum values directly derived from protein sequences. The feature importance analysis, using shapley additive explanations, further validated the advantages of ThermoFinder. We believe that ThermoFinder will be a valuable and comprehensive framework for predicting thermophilic proteins, and we have made our model open source and available on Github at https://github.com/Luo-SynBioLab/ThermoFinder.

ALDELE: All-Purpose Deep Learning Toolkits for Predicting the Biocatalytic Activities of Enzymes

Rapidly predicting enzyme properties for catalyzing specific substrates is essential for identifying potential enzymes for industrial transformations. The demand for sustainable production of valuable industry chemicals utilizing biological resources raised a pressing need to speed up biocatalyst screening using machine learning techniques. In this research, we developed an all-purpose deep-learning-based multiple-toolkit (ALDELE) workflow for screening enzyme catalysts. ALDELE incorporates both structural and sequence representations of proteins, alongside representations of ligands by subgraphs and overall physicochemical properties. Comprehensive evaluation demonstrated that ALDELE can predict the catalytic activities of enzymes, and particularly, it identifies residue-based hotspots to guide enzyme engineering and generates substrate heat maps to explore the substrate scope for a given biocatalyst. Moreover, our models notably match empirical data, reinforcing the practicality and reliability of our approach through the alignment with confirmed mutation sites. ALDELE offers a facile and comprehensive solution by integrating different toolkits tailored for different purposes at affordable computational cost and therefore would be valuable to speed up the discovery of new functional enzymes for their exploitation by the industry.

DLTKcat: deep learning-based prediction of temperature-dependent enzyme turnover rates

The enzyme turnover rate, ${k}_{cat}$, quantifies enzyme kinetics by indicating the maximum efficiency of enzyme catalysis. Despite its importance, ${k}_{cat}$ values remain scarce in databases for most organisms, primarily because of the cost of experimental measurements. To predict ${k}_{cat}$ and account for its strong temperature dependence, DLTKcat was developed in this study and demonstrated superior performance (log10-scale root mean squared error = 0.88, R-squared = 0.66) than previously published models. Through two case studies, DLTKcat showed its ability to predict the effects of protein sequence mutations and temperature changes on ${k}_{cat}$ values. Although its quantitative accuracy is not high enough yet to model the responses of cellular metabolism to temperature changes, DLTKcat has the potential to eventually become a computational tool to describe the temperature dependence of biological systems.

What can be lost? Genomic perspective on the lipid metabolism of Mucoromycota

Mucoromycota is a phylum of early diverging fungal (EDF) lineages, of mostly plant-associated terrestrial fungi. Some strains have been selected as promising biotechnological organisms due to their ability to produce polyunsaturated fatty acids and efficient conversion of nutrients into lipids. Others get their lipids from the host plant and are unable to produce even the essential ones on their own. Following the advancement in EDF genome sequencing, we carried out a systematic survey of lipid metabolism protein families across different EDF lineages. This enabled us to explore the genomic basis of the previously documented ability to produce several types of lipids within the fungal tree of life. The core lipid metabolism genes showed no significant diversity in distribution, however specialized lipid metabolic pathways differed in this regard among different fungal lineages. In total 165 out of 202 genes involved in lipid metabolism were present in all tested fungal lineages, while remaining 37 genes were found to be absent in some of fungal lineages. Duplications were observed for 69 genes. For the first time we demonstrate that ergosterol is not being produced by several independent groups of plant-associated fungi due to the losses of different ERG genes. Instead, they possess an ancestral pathway leading to the synthesis of cholesterol, which is absent in other fungal lineages. The lack of diacylglycerol kinase in both Mortierellomycotina and Blastocladiomycota opens the question on sterol equilibrium regulation in these organisms. Early diverging fungi retained most of beta oxidation components common with animals including Nudt7, Nudt12 and Nudt19 pointing at peroxisome divergence in Dikarya. Finally, Glomeromycotina and Mortierellomycotina representatives have a similar set of desaturases and elongases related to the synthesis of complex, polyunsaturated fatty acids pointing at an ancient expansion of fatty acid metabolism currently being explored by biotechnological studies.

Prediction of Enzyme Catalysis by Computing Reaction Energy Barriers via Steered QM/MM Molecular Dynamics Simulations and Machine Learning

The prediction of enzyme activity is one of the main challenges in catalysis. With computer-aided methods, it is possible to simulate the reaction mechanism at the atomic level. However, these methods are usually expensive if they are to be used on a large scale, as they are needed for protein engineering campaigns. To alleviate this situation, machine learning methods can help in the generation of predictive-decision models. Herein, we test different regression algorithms for the prediction of the reaction energy barrier of the rate-limiting step of the hydrolysis of mono-(2-hydroxyethyl)terephthalic acid by the MHETase ofIdeonella sakaiensis. As a training data set, we use steered quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulation snapshots and their corresponding pulling work values. We have explored three algorithms together with three chemical representations. As an outcome, our trained models are able to predict pulling works along the steered QM/MM MD simulations with a mean absolute error below 3 kcal mol-1 and a score value above 0.90. More challenging is the prediction of the energy maximum with a single geometry. Whereas the use of the initial snapshot of the QM/MM MD trajectory as input geometry yields a very poor prediction of the reaction energy barrier, the use of an intermediate snapshot of the former trajectory brings the score value above 0.40 with a low mean absolute error (ca. 3 kcal mol-1). Altogether, we have faced in this work some initial challenges of the final goal of getting an efficient workflow for the semiautomatic prediction of enzyme-catalyzed energy barriers and catalytic efficiencies.

Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine

Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.

Omics data for sampling thermodynamically feasible kinetic models

Kinetic models are key to understanding and predicting the dynamic behaviour of metabolic systems. Traditional models require kinetic parameters which are not always available and are often estimated in vitro. Ensemble models overcome this challenge by sampling thermodynamically feasible models around a measured reference point. However, it is unclear if the convenient distributions used to generate the ensemble produce a natural distribution of model parameters and hence if the model predictions are reasonable. In this paper, we produced a detailed kinetic model for the central carbon metabolism of Escherichia coli. The model consists of 82 reactions (including 13 reactions with allosteric regulation) and 79 metabolites. To sample the model, we used metabolomic and fluxomic data from a single steady-state time point for E. coli K-12 MG1655 growing on glucose minimal M9 medium (average sampling time for 1000 models: 11.21 ± 0.14 min). Afterwards, in order to examine whether our sampled models are biologically sound, we calculated Km, Vmax and kcat for the reactions and compared them to previously published values. Finally, we used metabolic control analysis to identify enzymes with high control over the fluxes in the central carbon metabolism. Our analyses demonstrate that our platform samples thermodynamically feasible kinetic models, which are in agreement with previously published experimental results and can be used to investigate metabolic control patterns within cells. This renders it a valuable tool for the study of cellular metabolism and the design of metabolic pathways.

Bayesian Optimization for Design of Multiscale Biological Circuits

Recent advances in synthetic biology have enabled the construction of molecular circuits that operate across multiple scales of cellular organization, such as gene regulation, signaling pathways, and cellular metabolism. Computational optimization can effectively aid the design process, but current methods are generally unsuited for systems with multiple temporal or concentration scales, as these are slow to simulate due to their numerical stiffness. Here, we present a machine learning method for the efficient optimization of biological circuits across scales. The method relies on Bayesian optimization, a technique commonly used to fine-tune deep neural networks, to learn the shape of a performance landscape and iteratively navigate the design space toward an optimal circuit. This strategy allows the joint optimization of both circuit architecture and parameters, and provides a feasible approach to solve a highly nonconvex optimization problem in a mixed-integer input space. We illustrate the applicability of the method on several gene circuits for controlling biosynthetic pathways with strong nonlinearities, multiple interacting scales, and using various performance objectives. The method efficiently handles large multiscale problems and enables parametric sweeps to assess circuit robustness to perturbations, serving as an efficient in silico screening method prior to experimental implementation.

From Spatial-Temporal Multiscale Modeling to Application: Bridging the Valley of Death in Industrial Biotechnology

The Valley of Death confronts industrial biotechnology with a significant challenge to the commercialization of products. Fortunately, with the integration of computation, automation and artificial intelligence (AI) technology, the industrial biotechnology accelerates to cross the Valley of Death. The Fourth Industrial Revolution (Industry 4.0) has spurred advanced development of intelligent biomanufacturing, which has evolved the industrial structures in line with the worldwide trend. To achieve this, intelligent biomanufacturing can be structured into three main parts that comprise digitalization, modeling and intellectualization, with modeling forming a crucial link between the other two components. This paper provides an overview of mechanistic models, data-driven models and their applications in bioprocess development. We provide a detailed elaboration of the hybrid model and its applications in bioprocess engineering, including strain design, process control and optimization, as well as bioreactor scale-up. Finally, the challenges and opportunities of biomanufacturing towards Industry 4.0 are also discussed.

Understudied proteins and understudied functions in the model bacterium Bacillus subtilis-A major challenge in current research

Model organisms such as the Gram-positive bacterium Bacillus subtilis have been studied intensively for decades. However, even for model organisms, no function has been identified for about one fourth of all proteins. It has recently been realized that such understudied proteins as well as poorly studied functions set a limitation to our understanding of the requirements for cellular life, and the Understudied Proteins Initiative has been launched. Of poorly studied proteins, those that are strongly expressed are likely to be important to the cell and should therefore be considered high priority in further studies. Since the functional analysis of unknown proteins can be extremely laborious, a minimal knowledge is required prior to targeted functional studies. In this review, we discuss strategies to obtain such a minimal annotation, for example, from global interaction, expression, or localization studies. We present a set of 41 highly expressed and poorly studied proteins of B. subtilis. Several of these proteins are thought or known to bind RNA and/or the ribosome, some may control the metabolism of B. subtilis, and another subset of particularly small proteins may act as regulatory elements to control the expression of downstream genes. Moreover, we discuss the challenges of poorly studied functions with a focus on RNA-binding proteins, amino acid transport, and the control of metabolic homeostasis. The identification of the functions of the selected proteins not only will strongly advance our knowledge on B. subtilis, but also on other organisms since many of the proteins are conserved in many groups of bacteria.

Archaea as a Model System for Molecular Biology and Biotechnology

Archaea represents the third domain of life, displaying a closer relationship with eukaryotes than bacteria. These microorganisms are valuable model systems for molecular biology and biotechnology. In fact, nowadays, methanogens, halophiles, thermophilic euryarchaeota, and crenarchaeota are the four groups of archaea for which genetic systems have been well established, making them suitable as model systems and allowing for the increasing study of archaeal genes' functions. Furthermore, thermophiles are used to explore several aspects of archaeal biology, such as stress responses, DNA replication and repair, transcription, translation and its regulation mechanisms, CRISPR systems, and carbon and energy metabolism. Extremophilic archaea also represent a valuable source of new biomolecules for biological and biotechnological applications, and there is growing interest in the development of engineered strains. In this review, we report on some of the most important aspects of the use of archaea as a model system for genetic evolution, the development of genetic tools, and their application for the elucidation of the basal molecular mechanisms in this domain of life. Furthermore, an overview on the discovery of new enzymes of biotechnological interest from archaea thriving in extreme environments is reported.

Nucleotide binding as an allosteric regulatory mechanism for Akkermansia muciniphila β-N-acetylhexosaminidase Am2136

β-N-acetylhexosaminidases (EC3.2.1.52), which belong to the glycosyl hydrolase family GH20, are important enzymes for oligosaccharides modification. Numerous microbial β-N-acetylhexosaminidases have been investigated for applications in biology, biomedicine and biotechnology. Akkermansia muciniphila is an anaerobic intestinal commensal bacterium which possesses specific β-N-acetylhexosaminidases for gut mucosal layer colonization and mucin degradation. In this study, we assessed the in vitro mucin glycan cleavage activity of the A. muciniphila β-N-acetylhexosaminidase Am2136 and demonstrated its ability that hydrolyzing the β-linkages joining N-acetylglucosamine to a wide variety of aglycone residues, which indicated that Am2136 may be a generalist β-N-acetylhexosaminidase. Structural and enzyme activity assay experiments allowed us to probe the essential function of the inter-domain interactions in β23-β33. Importantly, we revealed that the hydrolysis activity of Am2136 was enhanced by nucleotides. We further speculated that this activation mechanism might be associated with the conformational motions between domain III and IV. To our knowledge, this is the first report of nucleotide effector regulated β-N-acetylhexosaminidase, to reveal its novel biological functions. These findings contribute to understanding the distinct properties within the GH20 family and lay a certain foundation to develop controllable glycan hydrolyzing catalysts.Abbreviations: OD600 - optical cell densities at 600 nm; LB - Luria-Bertani; IPTG - isopropyl β-D-1-thiogalactopyranoside; PMSF - phenylmethanesulfonyl fluoride; rmsd - root mean square deviation; GlcNAc - N-acetyl-β-D-glucosamine; GalNAc - N-acetyl-β-D-galactosamine; Gal - galactose.

Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction

Enzyme turnover numbers (kcat) are key to understanding cellular metabolism, proteome allocation and physiological diversity, but experimentally measured kcat data are sparse and noisy. Here we provide a deep learning approach (DLKcat) for high-throughput kcat prediction for metabolic enzymes from any organism merely from substrate structures and protein sequences. DLKcat can capture kcat changes for mutated enzymes and identify amino acid residues with a strong impact on kcat values. We applied this approach to predict genome-scale kcat values for more than 300 yeast species. Additionally, we designed a Bayesian pipeline to parameterize enzyme-constrained genome-scale metabolic models from predicted kcat values. The resulting models outperformed the corresponding original enzyme-constrained genome-scale metabolic models from previous pipelines in predicting phenotypes and proteomes, and enabled us to explain phenotypic differences. DLKcat and the enzyme-constrained genome-scale metabolic model construction pipeline are valuable tools to uncover global trends of enzyme kinetics and physiological diversity, and to further elucidate cellular metabolism on a large scale.

Experimental and computational studies of cellulases as bioethanol enzymes

Bioethanol industries and bioprocesses have many challenges that constantly impede commercialization of the end product. One of the bottlenecks in the bioethanol industry is the challenge of discovering highly efficient catalysts that can improve biomass conversion. The current promising bioethanol conversion catalysts are microorganism-based cellulolytic enzymes, but lack optimization for high bioethanol conversion, due to biological and other factors. A better understanding of molecular underpinnings of cellulolytic enzyme mechanisms and significant ways to improve them can accelerate the bioethanol commercial production process. In order to do this, experimental methods are the primary choice to evaluate and characterize cellulase’s properties, but they are time-consuming and expensive. A time-saving, complementary approach involves computational methods that evaluate the same properties and improves our atomistic-level understanding of enzymatic mechanism of action. Theoretical methods in many cases have proposed research routes for subsequent experimental testing and validation, reducing the overall research cost. Having a plethora of tools to evaluate cellulases and the yield of the enzymatic process will aid in planning more optimized experimental setups. Thus, there is a need to connect the computational evaluation methods with the experimental methods to overcome the bottlenecks in the bioethanol industry. This review discusses various experimental and computational methods and their use in evaluating the multiple properties of cellulases.

Modeling and Optimization of a Molecular Biocontroller for the Regulation of Complex Metabolic Pathways

Achieving optimal production in microbial cell factories, robustness against changing intracellular and environmental perturbations requires the dynamic feedback regulation of the pathway of interest. Here, we consider a merging metabolic pathway motif, which appears in a wide range of metabolic engineering applications, including the production of phenylpropanoids among others. We present an approach to use a realistic model that accounts for in vivo implementation and then propose a methodology based on multiobjective optimization for the optimal tuning of the gene circuit parts composing the biomolecular controller and biosensor devices for a dynamic regulation strategy. We show how this approach can deal with the trade-offs between the performance of the regulated pathway, robustness to perturbations, and stability of the feedback loop. Using realistic models, our results suggest that the strategies for fine-tuning the trade-offs among performance, robustness, and stability in dynamic pathway regulation are complex. It is not always possible to infer them by simple inspection. This renders the use of the multiobjective optimization methodology valuable and necessary.

Integrative Genome-Scale Metabolic Modeling Reveals Versatile Metabolic Strategies for Methane Utilization in Methylomicrobium album BG8

Methylomicrobium album BG8 is an aerobic methanotrophic bacterium with promising features as a microbial cell factory for the conversion of methane to value-added chemicals. However, the lack of a genome-scale metabolic model (GEM) of M. album BG8 has hindered the development of systems biology and metabolic engineering of this methanotroph. To fill this gap, a high-quality GEM was constructed to facilitate a system-level understanding of the biochemistry of M. album BG8. Flux balance analysis, constrained with time-series data derived from experiments with various levels of methane, oxygen, and biomass, was used to investigate the metabolic states that promote the production of biomass and the excretion of carbon dioxide, formate, and acetate. The experimental and modeling results indicated that M. album BG8 requires a ratio of ∼1.5:1 between the oxygen- and methane-specific uptake rates for optimal growth. Integrative modeling revealed that at ratios of  >2:1 oxygen-to-methane uptake flux, carbon dioxide and formate were the preferred excreted compounds, while at ratios of <1.5:1 acetate accounted for a larger fraction of the total excreted flux. Our results showed a coupling between biomass production and the excretion of carbon dioxide that was linked to the ratio between the oxygen- and methane-specific uptake rates. In contrast, acetate excretion was experimentally detected during exponential growth only when the initial biomass concentration was increased. A relatively lower growth rate was also observed when acetate was produced in the exponential phase, suggesting a trade-off between biomass and acetate production.

IMPORTANCE A genome-scale metabolic model (GEM) is an integrative platform that enables the incorporation of a wide range of experimental data. It is used to reveal system-level metabolism and, thus, clarify the link between the genotype and phenotype. The lack of a GEM for Methylomicrobium album BG8, an aerobic methane-oxidizing bacterium, has hindered its use in environmental and industrial biotechnology applications. The diverse metabolic states indicated by the GEM developed in this study demonstrate the versatility in the methane metabolic processes used by this strain. The integrative GEM presented here will aid the implementation of the design-build-test-learn paradigm in the metabolic engineering of M. album BG8. This advance will facilitate the development of a robust methane bioconversion platform and help to mitigate methane emissions from environmental systems.

Functional Classification and Characterization of the Fungal Glycoside Hydrolase 28 Protein Family

Pectin is a major constituent of the plant cell wall, comprising compounds with important industrial applications such as homogalacturonan, rhamnogalacturonan and xylogalacturonan. A large array of enzymes is involved in the degradation of this amorphous substrate. The Glycoside Hydrolase 28 (GH28) family includes polygalacturonases (PG), rhamnogalacturonases (RG) and xylogalacturonases (XG) that share a structure of three to four pleated β-sheets that form a rod with the catalytic site amidst a long, narrow groove. Although these enzymes have been studied for many years, there has been no systematic analysis. We have collected a comprehensive set of GH28 encoding sequences to study their evolution in fungi, directed at obtaining a functional classification, as well as at the identification of substrate specificity as functional constraint. Computational tools such as Alphafold, Consurf and MEME were used to identify the subfamilies’ characteristics. A hierarchic classification defines the major classes of endoPG, endoRG and endoXG as well as three exoPG classes. Ascomycete endoPGs are further classified in two subclasses whereas we identify four exoRG subclasses. Diversification towards exomode is explained by loops that appear inserted in a number of turns. Substrate-driven diversification can be identified by various specificity determining positions that appear to surround the binding groove.

Cell-Free Multi-Enzyme Synthesis and Purification of Uridine Diphosphate Galactose

High costs and low availability of UDP-galactose hampers the enzymatic synthesis of valuable oligosaccharides such as human milk oligosaccharides. Here, we report the development of a platform for the scalable, biocatalytic synthesis and purification of UDP-galactose. UDP-galactose was produced with a titer of 48 mM (27.2 g/L) in a small-scale batch process (200 μL) within 24 h using 0.02 genzyme /gproduct . Through in-situ ATP regeneration, the amount of ATP (0.6 mM) supplemented was around 240-fold lower than the stoichiometric equivalent required to achieve the final product yield. Chromatographic purification using porous graphic carbon adsorbent yielded UDP-galactose with a purity of 92 %. The synthesis was transferred to 1 L preparative scale production in a stirred tank bioreactor. To further reduce the synthesis costs here, the supernatant of cell lysates was used bypassing expensive purification of enzymes. Here, 23.4 g/L UDP-galactose were produced within 23 h with a synthesis yield of 71 % and a biocatalyst load of 0.05 gtotal_protein /gproduct . The costs for substrates per gram of UDP-galactose synthesized were around 0.26 €/g.

Paludibacter propionicigenes GH10 xylanase as a tool for enzymatic xylooligosaccharides production from heteroxylans

Bioconversion of lignocellulosic biomass into value-added products relies on polysaccharides depolymerization by carbohydrate active enzymes. This work reports biochemical characterization of Paludibacter propionicigenes xylanase from GH10 (PpXyn10A) and its application for enzymatic xylooligosaccharides (XOS) production from commercial heteroxylans and liquor of hydrothermally pretreated corn cobs (PCC). PpXyn10A is tolerant to ethanol and NaCl, and releases xylobiose (X2) and xylotriose (X3) as the main hydrolytic products. The conversion rate of complex substrates into short XOS was approximately 30% for glucuronoxylan and 8.8% for rye arabinoxylan, after only 4 h; while for PCC, PpXyn10A greatly increased unbranched XOS yields. B. adolescentis fermentation with XOS from beechwood glucuronoxylan produced mainly acetic and lactic acids. Structural analysis shows that while the glycone region of PpXyn10A active site is well preserved, the aglycone region has aromatic interactions in the +2 subsite that may explain why PpXyn10A does not release xylose.

Hemodynamic molecular imaging of tumor-associated enzyme activity in the living brain

Molecular imaging could have great utility for detecting, classifying, and guiding treatment of brain disorders, but existing probes offer limited capability for assessing relevant physiological parameters. Here, we describe a potent approach for noninvasive mapping of cancer-associated enzyme activity using a molecular sensor that acts on the vasculature, providing a diagnostic readout via local changes in hemodynamic image contrast. The sensor is targeted at the fibroblast activation protein (FAP), an extracellular dipeptidase and clinically relevant biomarker of brain tumor biology. Optimal FAP sensor variants were identified by screening a series of prototypes for responsiveness in a cell-based bioassay. The best variant was then applied for quantitative neuroimaging of FAP activity in rats, where it reveals nanomolar-scale FAP expression by xenografted cells. The activated probe also induces robust hemodynamic contrast in nonhuman primate brain. This work thus demonstrates a potentially translatable strategy for ultrasensitive functional imaging of molecular targets in neuromedicine.

Mining the Genome of Bacillus velezensis VB7 (CP047587) for MAMP Genes and Non-Ribosomal Peptide Synthetase Gene Clusters Conferring Antiviral and Antifungal Activity

Chemical pesticides have an immense role in curbing the infection of plant viruses and soil-borne pathogens of high valued crops. However, the usage of chemical pesticides also contributes to the development of resistance among pathogens. Hence, attempts were made in this study to identify a suitable bacterial antagonist for managing viral and fungal pathogens infecting crop plants. Based on our earlier investigations, we identified Bacillus amyloliquefaciens VB7 as a potential antagonist for managing Sclerotinia sclerotiorum infecting carnation, tobacco streak virus infecting cotton and groundnut bud necrosis infecting tomato. Considering the multifaceted action of B. amyloliquefaciens VB7, attempts were made for whole-genome sequencing to assess the antiviral activity against tomato spotted wilt virus infecting chrysanthemum and antifungal action against Fusarium oxysporum f. sp. cubense (Foc). Genome annotation of the isolate B. amyloliquefaciens VB7 was confirmed as B. velezensis VB7 with accession number CP047587. Genome analysis revealed the presence of 9,231,928 reads with an average read length of 149 bp. Assembled genome had 1 contig, with a total length of 3,021,183 bp and an average G+C content of 46.79%. The protein-coding sequences (CDS) in the genome was 3090, transfer RNA (tRNA) genes were 85 with 29 ribosomal RNA (rRNA) genes and 21 repeat regions. The genome of B. velezensis VB7 had 506 hypothetical proteins and 2584 proteins with functional assignments. VB7 genome had the presence of flagellin protein FlaA with 987 nucleotides and translation elongation factor TU (Ef-Tu) with 1191 nucleotides. The identified ORFs were 3911 with 47.22% GC content. Non ribosomal pepide synthetase cluster (NRPS) gene clusters in the genome of VB7, coded for the anti-microbial peptides surfactin, butirosin A/butirosin B, fengycin, difficidin, bacillibactin, bacilysin, and mersacidin the Ripp lanthipeptide. Antiviral action of VB7 was confirmed by suppression of local lesion formation of TSWV in the local lesion host cowpea (Co-7). Moreover, combined application of B. velezensis VB7 with phyto-antiviral principles M. Jalapa and H. cupanioides increased shoot length, shoot diameter, number of flower buds per plant, flower diameter, and fresh weight of chrysanthemum. Further, screening for antifungal action of VB7 expressed antifungal action against Foc in vitro by producing VOC/NVOC compounds, including hexadecanoic acid, linoelaidic acid, octadecanoic acid, clindamycin, formic acid, succinamide, furanone, 4H-pyran, nonanol and oleic acid, contributing to the total suppression of Foc apart from the presence of NRPS gene clusters. Thus, our study confirmed the scope for exploring B. velezensis VB7 on a commercial scale to manage tomato spotted wilt virus, groundnut bud necrosis virus, tobacco streak virus, S. sclerotiorum, and Foc causing panama wilt of banana.

Influence of cosubstrate and hydraulic retention time on the removal of drugs and hygiene products in sanitary sewage in an anaerobic Expanded Granular Sludge Bed reactor

Diclofenac (DCF), ibuprofen (IBU), propranolol (PRO), triclosan (TCS) and linear alkylbenzene sulfonate (LAS) can be recalcitrant in Wastewater Treatment Plants (WWTP). The removal of these compounds was investigated in scale-up (69 L) Expanded Granular Sludge Bed (EGSB) reactor, fed with sanitary sewage from the São Carlos-SP (Brazil) WWTP and 200 mg L^-1 of ethanol. The EGSB was operated in three phases: (I) hydraulic retention time (HRT) of 36±4 h; (II) HRT of 20±2 h and (III) HRT of 20±2 h with ethanol. Phases I and II showed no significant difference in the removal of LAS (63 ± 11-65 ± 12 %), DCF (37 ± 18-35 ± 11 %), IBU (43 ± 18-44 ± 16 %) and PRO (46 ± 25-51 ± 23 %) for 13±2-15 ± 2 mg L^-1, 106 ± 32-462 ± 294 μg L^-1, 166 ± 55-462 ± 213 μg L^-1 and 201 ± 113-250 ± 141 μg L^-1 influent, respectively. Higher TCS removal was obtained in phase I (72 ± 17 % for 127 ± 120 μg L^-1 influent) when compared to phase II (51 ± 13 % for 135 ± 119 μg L^-1 influent). This was due to its greater adsorption (40 %) in the initial phase. Phase III had higher removal of DCF (42 ± 10 % for 107 ± 26 μg L^-1 influent), IBU (50 ± 15 % for 164 ± 47 μg L^-1 influent) and TCS (85 ± 15 % for 185 ± 148 μg L^-1 influent) and lower removal of LAS (35 ± 14 % for 12 ± 3 mg L^-1 influent) and PRO (-142 ± 177 % for 188 ± 88 μg L^-1 influent). Bacteria similar to Syntrophobacter, Smithella, Macellibacteroides, Syntrophus, Blvii28_wastewater-sludge_group and Bacteroides were identified in phase I with relative abundance of 3.1 %-4.7 %. Syntrophobacter was more abundant (15.4 %) in phase II, while in phase III, it was Smithella (12.7 %) and Caldisericum (15.1 %). Regarding the Archaea Domain, Methanosaeta was more abundant in phases I (84 %) and II (67 %), while in phase III it was Methanobacterium (86 %).

Manufacturing of Plant-Based Bioactive Peptides Using Enzymatic Methods to Meet Health and Sustainability Targets of the Sustainable Development Goals

Due to the rapid growth in the global population, the consumption of animal-based food products/food compounds has been associated with negative implications for food sustainability/security. As a result, there is an increasing demand for the development of plant-based food and compounds as alternatives. Meanwhile, a growing number of studies report the health benefits of food protein-based peptides prepared via enzymatic hydrolysis and exhibiting biological properties such as antioxidant, antihypertensive, anti-thrombotic, and antidiabetic activities. However, the inherent bitterness of some peptides hinders their application in food products as ingredients. This article aims to provide the latest findings on plant-based bioactive peptides, particularly their health benefits, manufacturing methods, detection and qualification of their bitterness properties, as well as debittering methods to reduce or eliminate this negative sensory characteristic. However, there is still a paucity of research on the biological property of debittered peptides. Therefore, the role of plant protein-derived bioactive peptides to meet the health targets of the Sustainable Development Goals can only be realised if advances are made in the industrial-scale bioprocessing and debittering of these peptides.

p38-mediated cell growth and survival drive rapid embryonic wound repair

Embryos repair wounds rapidly, with no inflammation or scarring, in a process that involves polarization of the actomyosin cytoskeleton. Actomyosin polarization results in the assembly of a contractile cable around the wound that drives wound closure. Here, we demonstrate that a contractile actomyosin cable is not sufficient for rapid wound repair in Drosophila embryos. We show that wounding causes activation of the serine/threonine kinase p38 mitogen-activated protein kinase (MAPK) in the cells adjacent to the wound. p38 activation reduces the levels of wound-induced reactive oxygen species in the cells around the wound, limiting wound size. In addition, p38 promotes an increase in volume in the cells around the wound, thus facilitating the collective cell movements that drive rapid wound healing. Our data indicate that p38 regulates cell volumes through the sodium-potassium-chloride cotransporter NKCC1. Our work reveals cell growth and cell survival as cell behaviors critical for embryonic wound repair.

Understanding the Xylooligosaccharides Utilization Mechanism of Lactobacillus brevis and Bifidobacterium adolescentis: Proteins Involved and Their Conformational Stabilities for Effectual Binding

Xylooligosaccharides having various degrees of polymerization such as xylobiose, xylotriose, and xylotetraose positively affect human health by interacting with gut proteins. The present study aimed to identify proteins present in gut microflora, such as xylosidase, xylulokinase, etc., with the help of retrieved whole-genome annotations and find out the mechanistic interactions of those with the above substrates. The 3D structures of proteins, namely Endo-1,4-beta-xylanase B (XynB) from Lactobacillus brevis and beta-D-xylosidase (Xyl3) from Bifidobacterium adolescentis, were computationally predicted and validated with the help of various bioinformatics tools. Molecular docking studies identified the effectual binding of these proteins to the xylooligosaccharides, and the stabilities of the best-docked complexes were analyzed by molecular dynamic simulation. The present study demonstrated that XynB and Xyl3 showed better effectual binding toward Xylobiose with the binding energies of - 5.96 kcal/mol and - 4.2 kcal/mol, respectively. The interactions were stabilized by several hydrogen bonding having desolvation energy (- 6.59 and - 7.91). The conformational stabilities of the docked complexes were observed in the four selected complexes of XynB-xylotriose, XynB-xylotetraose, Xyl3-xylobiose, and Xyn3-xylotriose by MD simulations. This study showed that the interactions of these four complexes are stable, which means they have complex metabolic activities among each other. Extending these studies of understanding, the interaction between specific probiotics enzymes and their ligands can explore the detailed design of synbiotics in the future.

A Tailor-Made Deazaflavin-Mediated Recycling System for Artificial Nicotinamide Cofactor Biomimetics

Nicotinamide adenine dinucleotide (NAD) and its 2'-phosphorylated form NADP are crucial cofactors for a large array of biocatalytically important redox enzymes. Their high cost and relatively poor stability, however, make them less attractive electron mediators for industrial processes. Nicotinamide cofactor biomimetics (NCBs) are easily synthesized, are inexpensive, and are also generally more stable than their natural counterparts. A bottleneck for the application of these artificial hydride carriers is the lack of efficient cofactor recycling methods. Therefore, we engineered the thermostable F420:NADPH oxidoreductase from Thermobifida fusca (Tfu-FNO), by structure-inspired site-directed mutagenesis, to accommodate the unnatural N1 substituents of eight NCBs. The extraordinarily low redox potential of the natural cofactor F420H2 was then exploited to reduce these NCBs. Wild-type enzyme had detectable activity toward all selected NCBs, with K m values in the millimolar range and k cat values ranging from 0.09 to 1.4 min-1. Saturation mutagenesis at positions Gly-29 and Pro-89 resulted in mutants with up to 139 times higher catalytic efficiencies. Mutant G29W showed a k cat value of 4.2 s-1 toward 1-benzyl-3-acetylpyridine (BAP+), which is similar to the k cat value for the natural substrate NADP+. The best Tfu-FNO variants for a specific NCB were then used for the recycling of catalytic amounts of these nicotinamides in conversion experiments with the thermostable ene-reductase from Thermus scotoductus (TsOYE). We were able to fully convert 10 mM ketoisophorone with BAP+ within 16 h, using F420 or its artificial biomimetic FOP (FO-2'-phosphate) as an efficient electron mediator and glucose-6-phosphate as an electron donor. The generated toolbox of thermostable and NCB-dependent Tfu-FNO variants offers powerful cofactor regeneration biocatalysts for the reduction of several artificial nicotinamide biomimetics at both ambient and high temperatures. In fact, to our knowledge, this enzymatic method seems to be the best-performing NCB-recycling system for BNAH and BAPH thus far.

Functions of elements in soil microorganisms

The soil microbial community fulfils various functions, such as nutrient cycling and carbon (C) sequestration, therefore contributing to maintenance of soil fertility and mitigation of global warming. In this context, a major focus of research has been on C, nitrogen (N) and phosphorus (P) cycling. However, from aquatic and other environments, it is well known that other elements beyond C, N, and P are essential for microbial functioning. Nonetheless, for soil microorganisms this knowledge has not yet been synthesised. To gain a better mechanistic understanding of microbial processes in soil systems, we aimed at summarising the current knowledge on the function of a range of essential or beneficial elements, which may affect the efficiency of microbial processes in soil. This knowledge is discussed in the context of microbial driven nutrient and C cycling. Our findings may support future investigations and data evaluation, where other elements than C, N, and P affect microbial processes.

Enzyme Promiscuity Prediction Using Hierarchy-Informed Multi-Label Classification

MOTIVATION

As experimental efforts are costly and time consuming, computational characterization of enzyme capabilities is an attractive alternative. We present and evaluate several machine-learning models to predict which of 983 distinct enzymes, as defined via the Enzyme Commission (EC) numbers, are likely to interact with a given query molecule. Our data consists of enzyme-substrate interactions from the BRENDA database. Some interactions are attributed to natural selection and involve the enzyme's natural substrates. The majority of the interactions however involve non-natural substrates, thus reflecting promiscuous enzymatic activities.

RESULTS

We frame this "enzyme promiscuity prediction" problem as a multi-label classification task. We maximally utilize inhibitor and unlabelled data to train prediction models that can take advantage of known hierarchical relationships between enzyme classes. We report that a hierarchical multi-label neural network, EPP-HMCNF, is the best model for solving this problem, outperforming k-nearest neighbours similarity-based and other machine learning models. We show that inhibitor information during training consistently improves predictive power, particularly for EPP-HMCNF. We also show that all promiscuity prediction models perform worse under a realistic data split when compared to a random data split, and when evaluating performance on non-natural substrates compared to natural substrates.

AVAILABILITY AND IMPLEMENTATION

We provide Python code for EPP-HMCNF and other models in a repository termed EPP (Enzyme Promiscuity Prediction) at https://github.com/hassounlab/EPP.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Influence of metabolic cosubstrates on methanogenic potential and degradation of triclosan and propranolol in sanitary sewage

Triclosan (TCS) and propranolol (PRO) are emerging micropollutants that are difficult to remove in wastewater treatment plants. In this study, methanogenic potential (P) of anaerobic sludge submitted to TCS (3.6 ± 0.1 to 15.5 ± 0.1 mg L^-1) and PRO (6.1 ± 0.1 to 55.9 ± 1.2 mg L^-1) in sanitary sewage, was investigated in batch reactors. The use of cosubstrates (200 mg L^-1 of organic matter) ethanol, methanol:ethanol and fumarate was evaluated for micropollutant degradation. Without cosubstrates, P values for 5.0 ± 0.1 mgTCS L^-1, 15.5 ± 0.1 mgTCS L^-1 and 55.0 ± 1.3 mgPRO L^-1 were 50.53%, 98.24% and 17.66% lower in relation to Control assay (855 ± 5 μmolCH4) with sanitary sewage, without micropollutants and cosubstrates, respectively. The use of fumarate, ethanol and methanol:ethanol favored greater methane production, with P values of 2144 ± 45 μmolCH₄, 2960 ± 185 μmolCH₄ and 2239 ± 171 μmolCH₄ for 5.1 ± 0.1 mgTCS L^-1, respectively; and of 10,827 ± 185 μmolCH₄, 10,946 ± 108 μmolCH₄ and 10,809 ± 210 μmolCH₄ for 55.0 ± 1.3 mgPRO L^-1, respectively. Greater degradation of TCS (77.1 ± 0.1% for 5.1 ± 0.1 mg L^-1) and PRO (24.1 ± 0.1% for 55.9 ± 1.2 mg L^-1) was obtained with ethanol. However, with 28.5 ± 0.5 mg PRO L^-1, greater degradation (88.4 ± 0.9%) was obtained without cosubstrates. With TCS, via sequencing of rRNA 16S gene, for Bacteria Domain, greater abundance of phylum Chloroflexi and of the genera Longilinea, Arcobacter, Mesotoga and Sulfuricurvum were identified. With PRO, the genus VadinBC27 was the most abundant. Methanosaeta was dominant in TCS with ethanol, while in PRO without cosubstrates, Methanobacterium and Methanosaeta were the most abundant. The use of metabolic cosubstrates is a favorable strategy to obtain greater methanogenic potential and degradation of TCS and PRO.

BEST: a Shiny/R web-based application to easily retrieve cross-related enzyme functional parameters and information from BRENDA

MOTIVATION

BRENDA is the largest enzyme functional database, containing information of 84 000 experimentally characterized enzyme entries. This database is an invaluable resource for researchers in the biological field, which classifies enzyme-related information in categories that are very useful to obtain specific functional and protein engineering information for enzyme families. However, the BRENDA web interface, the most used by researchers with a non-informatic background, does not allow the user to cross-reference data from different categories or sub-categories in the database. Obtaining information in an easy and fast way, in a friendly web interface, without the necessity to have a deep informatics knowledge, will facilitate and improve research in the enzymology and protein engineering field.

RESULTS

We developed the Brenda Easy Search Tool (BEST), an interactive Shiny/R application that enables querying the BRENDA database for complex cross-tabulated characteristics, and retrieving enzyme-related parameters and information readily and efficiently, which can be used for the study of enzyme function or as an input for other bioinformatics tools.

AVAILABILITY AND IMPLEMENTATION

BEST and its tutorial are freely available from https://pesb2.cl/best/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An Ontology-Driven Personalized Faceted Search for Exploring Knowledge Bases of Capsicum

Capsicum is a genus of flowering plants in the Solanaceae family in which the members are well known to have a high economic value. The Capsicum fruits, which are popularly known as peppers or chili, have been widely used by people worldwide. It serves as a spice and raw material for many products such as sauce, food coloring, and medicine. For many years, scientists have studied this plant to optimize its production. A tremendous amount of knowledge has been obtained and shared, as reflected in multiple knowledge-based systems, databases, or information systems. An approach to knowledge-sharing is through the adoption of a common ontology to eliminate knowledge understanding discrepancy. Unfortunately, most of the knowledge-sharing solutions are intended for scientists who are familiar with the subject. On the other hand, there are groups of potential users that could benefit from such systems but have minimal knowledge of the subject. For these non-expert users, finding relevant information from a less familiar knowledge base would be daunting. More than that, users have various degrees of understanding of the available content in the knowledge base. This understanding discrepancy raises a personalization problem. In this paper, we introduce a solution to overcome this challenge. First, we developed an ontology to facilitate knowledge-sharing about Capsicum to non-expert users. Second, we developed a personalized faceted search algorithm that provides multiple structured ways to explore the knowledge base. The algorithm addresses the personalization problem by identifying the degree of understanding about the subject from each user. In this way, non-expert users could explore a knowledge base of Capsicum efficiently. Our solution characterized users into four groups. As a result, our faceted search algorithm defines four types of matching mechanisms, including three ranking mechanisms as the core of our solution. In order to evaluate the proposed method, we measured the predictability degree of produced list of facets. Our findings indicated that the proposed matching mechanisms could tolerate various query types, and a high degree of predictability can be achieved by combining multiple ranking mechanisms. Furthermore, it demonstrates that our approach has a high potential contribution to biodiversity science in general, where many knowledge-based systems have been developed with limited access to users outside of the domain.

The moonlighting activities of dihydrolipoamide dehydrogenase: Biotechnological and biomedical applications

Dihydrolipoamide dehydrogenase (DLDH) is a homodimeric flavin-dependent enzyme that catalyzes the NAD⁺ -dependent oxidation of dihydrolipoamide. The enzyme is part of several multi-enzyme complexes such as the Pyruvate Dehydrogenase system that transforms pyruvate into acetyl-co-A. Concomitantly with its redox activity, DLDH produces Reactive Oxygen Species (ROS), which are involved in cellular apoptotic processes. DLDH possesses several moonlighting functions. One of these is the capacity to adhere to metal-oxides surfaces. This was first exemplified by the presence of an exocellular form of the enzyme on the cell-wall surface of Rhodococcus ruber. This capability was evolutionarily conserved and identified in the human, mitochondrial, DLDH. The enzyme was modified with Arg-Gly-Asp (RGD) groups, which enabled its interaction with integrin-rich cancer cells followed by "integrin-assisted-endocytosis." This allowed harnessing the enzyme for cancer therapy. Combining the TiO₂ -binding property with DLDH's ROS-production, enabled us to develop several medical applications including improving oesseointegration of TiO₂ -based implants and photodynamic treatment for melanoma. The TiO₂ -binding sites of both the bacterial and human DLDH's were identified on the proteins' molecules at regions that overlap with the binding site of E3-binding protein (E3BP). This protein is essential in forming the multiunit structure of PDC. Another moonlighting activity of DLDH, which is described in this Review, is its DNA-binding capacity that may affect DNA chelation and shredding leading to apoptotic processes in living cells. The typical ROS-generation by DLDH, which occurs in association with its enzymatic activity and its implications in cancer and apoptotic cell death are also discussed.

A Novel Tyrosinase from Armillaria ostoyae with Comparable Monophenolase and Diphenolase Activities Suffers Substrate Inhibition

Tyrosinase is a bifunctional enzyme mediating the o-hydroxylation and two-electron oxidation of monophenols to o-quinones. The monophenolase activity of tyrosinase is much desired for the industrial synthesis of catechols. However, the generally low ratio of monophenolase/diphenolase activity of tyrosinase limited its utilization in the industry. In this study, a novel tyrosinase from Armillaria ostoyae strain C18/9 (AoTyr) was characterized, and the results showed that the enzyme has an optimal temperature of 25°C and an optimal pH of 6. The enzyme has comparable monophenolase and diphenolase activities and exhibits substrate inhibition in both of the activities. In silico analysis and mutagenesis experiments showed that residues 262 and 266 play important roles in modulating the substrate inhibition and enzymatic activities of AoTyr, and the replacement of D262 with asparagine significantly increased the monophenolase/diphenolase catalytic efficiencies (k_cat/K_m ratios) (1.63-fold) of the enzyme. The results from this study indicated that this novel tyrosinase could be a potential candidate for the industrial biosynthesis of catechols. IMPORTANCE Tyrosinase is able to oxidize various phenolic compounds, and its ability to convert monophenols into diphenols has caught great attention in the research field and industrial applications. However, the utilization of tyrosinase for the industrial synthesis of catechols has been limited due to the fact that the monophenolase activity of most of the known tyrosinases is much lower than the diphenolase activity. In the present study, a novel tyrosinase with comparable monophenolase and diphenolase activities was characterized. The enzyme exhibits substrate inhibition in both monophenolase and diphenolase activities. In silico analysis followed by mutagenesis experiments confirmed the important roles of residues 262 and 266 in the substrate inhibition and activity modulation of the enzyme, and the D262N variant showed an enhanced monophenolase/diphenolase catalytic efficiency ratio compared to the wild-type enzyme.

The synergistic actions of hydrolytic genes reveal the mechanism of Trichoderma harzianum for cellulose degradation

Bioprospecting genes and proteins related to plant biomass degradation is an attractive approach for the identification of target genes for biotechnological purposes, especially those with potential applications in the biorefinery industry that can enhance second-generation ethanol production technology. Trichoderma harzianum is a potential candidate for cellulolytic enzyme prospection and production. Herein, the enzymatic activities, transcriptome, exoproteome, and coexpression networks of the T. harzianum strain CBMAI-0179 were examined under biomass degradation conditions. We identified differentially expressed genes (DEGs) and carbohydrate-active enzyme (CAZyme) genes related to plant biomass degradation and compared them with those of strains from congeneric species (T. harzianum IOC-3844 and T. atroviride CBMAI-0020). T. harzianum CBMAI-0179 harbors strain- and treatment-specific CAZyme genes and transcription factors. We detected important proteins related to biomass degradation, including β-glucosidases, endoglucanases, cellobiohydrolases, lytic polysaccharide monooxygenases, endo-1,4-β-xylanases and β-mannanases. Based on coexpression networks, an enriched cluster with degradative enzymes was described, and the subnetwork of CAZymes revealed strong correlations among important secreted proteins and differentially expressed CAZyme genes. Our results provide valuable information for future studies on the genetic regulation of plant cell wall-degrading enzymes. This knowledge can be exploited for the improvement of enzymatic reactions in biomass degradation for bioethanol production.

Identification of immucillin analogue natural compounds to inhibit Helicobacter pylori MTAN through high throughput virtual screening and molecular dynamics simulation

Abstract

One in every two humans is having Helicobacter pylori (H. pylori) in stomach causing gastric ulcer. Emergence of several drugs in eliminating H. pylori has paved way for emergence of multidrug resistance in them. This resistance is thriving and thereby necessitating the need of a potent drug. Identifying a potential target for medication is crucial. Bacterial 5'-methylthioadenosine/S-enosyl homocysteine nucleosidase (MTAN) is a multifunctional enzyme that controls seven essential metabolic pathways. It functions as a catalyst in the hydrolysis of the N-ribosidic bond of adenosine-based metabolites: S-adenosylhomocysteine (SAH), 5'-methylthioadenosine (MTA), 5'-deoxyadenosine (5'-DOA), and 6-amino-6-deoxyfutalosine. H. pylori unlike other bacteria and humans utilises an alternative pathway for menaquinone synthesis. It utilises Futosiline pathway for menaquinone synthesis which are obligatory component in electron transport pathway. Therefore, the enzymes functioning in this pathway represent them-self as a point of attack for new medications. We targeted MTAN protein of H. pylori to find out a potent natural hit to inhibit its growth. A comparative analysis was made with potent H. pylori MTAN (HpMTAN) known inhibitor, 5'-butylthio-DADMe-Immucillin-A (BuT-DADMe-ImmA) and ZINC natural subset database. Optimized ligands from the ZINC natural database were virtually screened using ligand based pharmacophore hypothesis to obtain the most efficient and potent inhibitors for HpMTAN. The screened leads were evaluated for their therapeutic likeness. Furthermore, the ligands that passed the test were subjected for MM-GBSA with MTAN to reveal the essential features that contributes selectivity. The results showed that Van der Waals contributions play a central role in determining the selectivity of MTAN. Molecular dynamics (MD) studies were carried out for 100 ns to assess the stability of ligands in the active site. MD analysis showed that binding of ZINC00490333 with MTAN is stable compared to reference inhibitor molecule BuT-DADMe-ImmA. Among the natural inhibitors screened after various docking procedures ZINC00490333 has highest binding score for HpMTAN (- 13.987). The ZINC inhibitor was successful in reproducing the BuT-DADMe-ImmA interactions with HpMTAN. Hence we suggest that ZINC00490333 compound may represent as a good lead in designing novel potent inhibitors of HpMTAN. This in silico approach indicates the potential of this molecule for advancing a further step in gastric ulcer treatment.

Graphic abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-021-00081-2.