MCIC: Automated Identification of Cellulases From Metagenomic Data and Characterization Based on Temperature and pH Dependence

Promoting lignocellulosic biorefinery by machine learning: progress, perspectives and challenges

The lignocellulosic biorefinery involves pretreatment, enzymatic hydrolysis, mixed sugar fermentation, and optional anaerobic digestion. This pipeline could be effectively implemented through machine learning (ML)-guided process optimization and strain modification rather than experimental or experience-based ones. This review takes a holistic perspective on the entire pipeline, discussing how ML could aid lignocellulosic, while other published work has focused on individual modules within the pipeline. This review also explores the model construction and evaluation strategies and highlights the emerging potential of transfer learning and hybrid ML models to address data insufficiency and improve model interpretability. Furthermore, challenges and future prospects of ML in lignocellulosic biorefinery will be elaborated in this review. Integrating ML into lignocellulosic biorefinery offers a promising pathway towards sustainable and competitive biorefinery systems.

Metagenomic exploration and computational prediction of novel enzymes for polyethylene terephthalate degradation

As a global environmental challenge, plastic pollution raises serious ecological and health concerns owing to the excessive accumulation of plastic waste, which disrupts ecosystems, harms wildlife, and threatens human health. Polyethylene terephthalate (PET), one of the most commonly used plastics, has contributed significantly to this growing crisis. This study offers a solution for plastic pollution by identifying novel PET-degrading enzymes. Using a combined approach of computational analysis and metagenomic workflow, we identified a diverse array of genes and enzymes linked to plastic degradation. Our study identified 1305,282 unmapped genes, 36,000 CAZymes, and 317 plastizymes in the soil samples were heavily contaminated with plastic. We extended our approach by training machine learning models to discover candidate PET-degrading enzymes. To overcome the scarcity of known PET-degrading enzymes, we used a Generative Adversarial Network (GAN) model for dataset augmentation and a pretrained deep Evolutionary Scale Language Model (ESM) to generate sequence embeddings for classification. Finally, 21 novel PET-degrading enzymes were identified. These enzymes were further validated through active site analysis, amino acid composition analysis, and 3D structure comparison. Additionally, we isolated bacterial strains from contaminated soils and extracted plastizymes to demonstrate their potential for environmental remediation. This study highlights the importance of biotechnological solutions for plastic pollution, emphasizing scalable, cost-effective processes and the integration of computational and metagenomic methods.

Enhancing nutritional and potential antimicrobial properties of poultry feed through encapsulation of metagenome-derived multi-enzymes

BACKGROUND

The encapsulation of metagenome-derived multi-enzymes presents a novel approach to improving poultry feed by enhancing nutrient availability and reducing anti-nutritional factors. By integrating and encapsulated enzymes such as carbohydrate-hydrolyzing enzymes, protease, lipase, and laccase into feed formulations, this method not only improves feed digestibility but also potentially contributes to animal health and productivity through antimicrobial properties.

RESULTS

This study investigates the encapsulation of metagenome-derived enzymes, including carbohydrate-hydrolyzing enzymes, protease, lipase, and laccase, using Arabic and Guar gums as encapsulating agents. The encapsulated multi-enzymes exhibited significant antimicrobial activity, achieving a 92.54% inhibition rate against Escherichia coli at a concentration of 6 U/mL. Fluorescence tracking with FITC-labeled enzymes confirmed efficient encapsulation and distribution, while physical characterization, including moisture content and solubility assessments, along with Atomic Force Microscopy (AFM) imaging, validated successful encapsulation. The encapsulated enzymes also effectively hydrolyzed poultry feed, leading to an increase in phenolic content and antioxidant activity, as confirmed by 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid (ABTS) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) assays.

CONCLUSIONS

The encapsulated multi-enzymes improved the overall feed quality by increasing reducing sugars and enhancing physical properties such as solubility and water-holding capacity. The encapsulated multi-enzymes improved the overall feed quality by increasing reducing sugars, antioxidant activity and enhancing physical properties such as solubility and water-holding capacity. Scanning Electron Microscopy (SEM) and Fourier-Transform Infrared Spectroscopy (FTIR) analyses confirmed the enzymatic breakdown of the feed structure. These results suggest that supplementing poultry feed with encapsulated multi-enzymes can enhance its physical, nutritional, and functional properties, leading to improved digestibility and overall feed quality.

Precision enzyme discovery through targeted mining of metagenomic data

Metagenomics has opened new avenues for exploring the genetic potential of uncultured microorganisms, which may serve as promising sources of enzymes and natural products for industrial applications. Identifying enzymes with improved catalytic properties from the vast amount of available metagenomic data poses a significant challenge that demands the development of novel computational and functional screening tools. The catalytic properties of all enzymes are primarily dictated by their structures, which are predominantly determined by their amino acid sequences. However, this aspect has not been fully considered in the enzyme bioprospecting processes. With the accumulating number of available enzyme sequences and the increasing demand for discovering novel biocatalysts, structural and functional modeling can be employed to identify potential enzymes with novel catalytic properties. Recent efforts to discover new polysaccharide-degrading enzymes from rumen metagenome data using homology-based searches and machine learning-based models have shown significant promise. Here, we will explore various computational approaches that can be employed to screen and shortlist metagenome-derived enzymes as potential biocatalyst candidates, in conjunction with the wet lab analytical methods traditionally used for enzyme characterization.

A metagenomic catalog for exploring the plastizymes landscape covering taxa, genes, and proteins

There are significant environmental and health concerns associated with the current inefficient plastic recycling process. This study presents the first integrated reference catalog of plastic-contaminated environments obtained using an insilico workflow that could play a significant role in discovering new plastizymes. Here, we combined 66 whole metagenomic data from plastic-contaminated environment samples from four previously collected metagenome data with our new sample. In this study, an integrated plastic-contaminated environment gene, protein, taxa, and plastic degrading enzyme catalog (PDEC) was constructed. These catalogs contain 53,300,583 non-redundant genes and proteins, 691 metagenome-assembled genomes, and 136,654 plastizymes. Based on KEGG and eggNOG annotations, 42% of recognized genes lack annotations, indicating their functions remain elusive and warrant further investigation. Additionally, the PDEC catalog highlights hydrolases, peroxidases, and cutinases as the prevailing plastizymes. Ultimately, following multiple validation procedures, our effort focused on pinpointing enzymes that exhibited the highest similarity to the introduced plastizymes in terms of both sequence and three-dimensional structural aspects. This encompassed evaluating the linear composition of constituent units as well as the complex spatial conformation of the molecule. The resulting catalog is expected to improve the resolution of future multi-omics studies, providing new insights into plastic-pollution related research.

Lignocellulose degradation by rumen bacterial communities: New insights from metagenome analyses

Ruminant animals house a dense and diverse community of microorganisms in their rumen, an enlarged compartment in their stomach, which provides a supportive environment for the storage and microbial fermentation of ingested feeds dominated by plant materials. The rumen microbiota has acquired diverse and functionally overlapped enzymes for the degradation of plant cell wall polysaccharides. In rumen Bacteroidetes, enzymes involved in degradation are clustered into polysaccharide utilization loci to facilitate coordinated expression when target polysaccharides are available. Firmicutes use free enzymes and cellulosomes to degrade the polysaccharides. Fibrobacters either aggregate lignocellulose-degrading enzymes on their cell surface or release them into the extracellular medium in membrane vesicles, a mechanism that has proven extremely effective in the breakdown of recalcitrant cellulose. Based on current metagenomic analyses, rumen Bacteroidetes and Firmicutes are categorized as generalist microbes that can degrade a wide range of polysaccharides, while other members adapted toward specific polysaccharides. Particularly, there is ample evidence that Verrucomicrobia and Spirochaetes have evolved enzyme systems for the breakdown of complex polysaccharides such as xyloglucans, peptidoglycans, and pectin. It is concluded that diversity in degradation mechanisms is required to ensure that every component in feeds is efficiently degraded, which is key to harvesting maximum energy by host animals.

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

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

Efficient bioconversion of lignocellulosic waste by a novel computationally screened hyperthermostable enzyme from a specialized microbiota

A large amount of lignocellulosic waste is generated every day in the world, and their accumulation in the agroecosystems, integration in soil compositions, or incineration for energy production has severe environmental pollution effects. Using enzymes as biocatalysts for the biodegradation of lignocellulosic materials, especially in harsh processing conditions, is a practical step towards green energy and environmental biosafety. Hence, the current study focuses on enzyme computationally screened from camel rumen metagenomics data as specialized microbiota that have the capacity to degrade lignocellulosic-rich and recalcitrant materials. The novel hyperthermostable xylanase named PersiXyn10 with the performance at extreme conditions was proper activity within a broad temperature (30-100 ℃) and pH range (4.0-11.0) but showed the maximum xylanolytic activity in severe alkaline and temperature conditions, pH 8.0 and temperature 90 ℃. Also, the enzyme had highly resistant to metals, surfactants, and organic solvents in optimal conditions. The introduced xylanase had unique properties in terms of thermal stability by maintaining over 82% of its activity after 15 days of incubation at 90 ℃. Considering the crucial role of hyperthermostable xylanases in the paper industry, the PersiXyn10 was subjected to biodegradation of paper pulp. The proper performance of hyperthermostable PersiXyn10 on the paper pulp was confirmed by structural analysis (SEM and FTIR) and produced 31.64 g/L of reducing sugar after 144 h hydrolysis. These results proved the applicability of the hyperthermostable xylanase in biobleaching and saccharification of lignocellulosic biomass for declining the environmental hazards.

Enhancing the ethanol production by exploiting a novel metagenomic-derived bifunctional xylanase/β-glucosidase enzyme with improved β-glucosidase activity by a nanocellulose carrier

Some enzymes can catalyze more than one chemical conversion for which they are physiologically specialized. This secondary function, which is called underground, promiscuous, metabolism, or cross activity, is recognized as a valuable feature and has received much attention for developing new catalytic functions in industrial applications. In this study, a novel bifunctional xylanase/β-glucosidase metagenomic-derived enzyme, PersiBGLXyn1, with underground β-glucosidase activity was mined by in-silico screening. Then, the corresponding gene was cloned, expressed and purified. The PersiBGLXyn1 improved the degradation efficiency of organic solvent pretreated coffee residue waste (CRW), and subsequently the production of bioethanol during a separate enzymatic hydrolysis and fermentation (SHF) process. After characterization, the enzyme was immobilized on a nanocellulose (NC) carrier generated from sugar beet pulp (SBP), which remarkably improved the underground activity of the enzyme up to four-fold at 80°C and up to two-fold at pH 4.0 compared to the free one. The immobilized PersiBGLXyn1 demonstrated 12 to 13-fold rise in half-life at 70 and 80°C for its underground activity. The amount of reducing sugar produced from enzymatic saccharification of the CRW was also enhanced from 12.97 g/l to 19.69 g/l by immobilization of the enzyme. Bioethanol production was 29.31 g/l for free enzyme after 72 h fermentation, while the immobilized PersiBGLXyn1 showed 51.47 g/l production titre. Overall, this study presented a cost-effective in-silico metagenomic approach to identify novel bifunctional xylanase/β-glucosidase enzyme with underground β-glucosidase activity. It also demonstrated the improved efficacy of the underground activities of the bifunctional enzyme as a promising alternative for fermentable sugars production and subsequent value-added products.

Simultaneous hydrolysis of various protein-rich industrial wastes by a naturally evolved protease from tannery wastewater microbiota

Elimination of protein-rich waste materials is one of the vital environmental protection requirements. Using of non-naturally occurring chemicals for their remediation properties can potentially induce new pollutants. Therefore, enzymes encoded in the genomes of microorganisms evolved in the same environment can be considered suitable alternatives to chemicals. Identification of efficient proteases that can hydrolyze recalcitrant, protein-rich wastes produced by various industrial processes has been widely welcomed as an eco-friendly waste management strategy. In this direction, we attempted to screen a thermo-halo-alkali-stable metagenome-derived protease (PersiProtease1) from tannery wastewater. The PersiProtease1 exhibited high pH stability over a wide range and at 1 h in pH 11.0 maintained 87.59% activity. The enzyme possessed high thermal stability while retaining 76.64% activity after 1 h at 90 °C. Moreover, 65.34% of the initial activity of the enzyme remained in the presence of 6 M NaCl, showing tolerance against high salinity. The presence of various metal ions, inhibitors, and organic solvents did not remarkably inhibit the activity of the discovered protease. The PersiProtease1 was extracted from the tannery wastewater microbiota and efficiently applied for biodegradation of real sample tannery wastewater protein, chicken feathers, whey protein, dehairing sheepskins, and waste X-ray films. PersiProtease1 proved its enormous potential in simultaneous biodegradation of solid and liquid protein-rich industrial wastes based on the results.

Highly efficient removal of dyes from wastewater using nanocellulose from quinoa husk as a carrier for immobilization of laccase

In this study, the synthesis of nanocellulose (NC) from an agro-waste of quinoa husks (QS) was reported for the first time. The NC nano-carrier was utilized for immobilization of a model laccase enzyme (PersiLac1) providing an innovative, green, and practical nano-biocatalyst for efficient removal of two different model dyes (malachite green (MG) and congo red (CR)) from water. This nano-biocatalyst developed a synergistic adsorption-degradation approach leading the dye molecules easily gathered near the nano-carrier by adsorption and then degraded effectively by the enzyme. Upon enzyme immobilization, the dye removals (%) were remarkably improved for both 150 mg/L of dyes (from 54% and 12%, for MG and CR, respectively, in case of the pristine NCs, to 98% and 60% for the immobilized enzyme). The immobilized PersiLac1 could decolorize the concentrated dye solutions and showed superior reusability (up to 83% dye removal after 18th runs for MG) and remarkable performance from complex real textile effluents.

Multi-label classification of research articles using Word2Vec and identification of similarity threshold

Every year, around 28,100 journals publish 2.5 million research publications. Search engines, digital libraries, and citation indexes are used extensively to search these publications. When a user submits a query, it generates a large number of documents among which just a few are relevant. Due to inadequate indexing, the resultant documents are largely unstructured. Publicly known systems mostly index the research papers using keywords rather than using subject hierarchy. Numerous methods reported for performing single-label classification (SLC) or multi-label classification (MLC) are based on content and metadata features. Content-based techniques offer higher outcomes due to the extreme richness of features. But the drawback of content-based techniques is the unavailability of full text in most cases. The use of metadata-based parameters, such as title, keywords, and general terms, acts as an alternative to content. However, existing metadata-based techniques indicate low accuracy due to the use of traditional statistical measures to express textual properties in quantitative form, such as BOW, TF, and TFIDF. These measures may not establish the semantic context of the words. The existing MLC techniques require a specified threshold value to map articles into predetermined categories for which domain knowledge is necessary. The objective of this paper is to get over the limitations of SLC and MLC techniques. To capture the semantic and contextual information of words, the suggested approach leverages the Word2Vec paradigm for textual representation. The suggested model determines threshold values using rigorous data analysis, obviating the necessity for domain expertise. Experimentation is carried out on two datasets from the field of computer science (JUCS and ACM). In comparison to current state-of-the-art methodologies, the proposed model performed well. Experiments yielded average accuracy of 0.86 and 0.84 for JUCS and ACM for SLC, and 0.81 and 0.80 for JUCS and ACM for MLC. On both datasets, the proposed SLC model improved the accuracy up to 4%, while the proposed MLC model increased the accuracy up to 3%.

The Methods of Digging for "Gold" within the Salt: Characterization of Halophilic Prokaryotes and Identification of Their Valuable Biological Products Using Sequencing and Genome Mining Tools

Halophiles, the salt-loving organisms, have been investigated for at least a hundred years. They are found in all three domains of life, namely Archaea, Bacteria, and Eukarya, and occur in saline and hypersaline environments worldwide. They are already a valuable source of various biomolecules for biotechnological, pharmaceutical, cosmetological and industrial applications. In the present era of multidrug-resistant bacteria, cancer expansion, and extreme environmental pollution, the demand for new, effective compounds is higher and more urgent than ever before. Thus, the unique metabolism of halophilic microorganisms, their low nutritional requirements and their ability to adapt to harsh conditions (high salinity, high pressure and UV radiation, low oxygen concentration, hydrophobic conditions, extreme temperatures and pH, toxic compounds and heavy metals) make them promising candidates as a fruitful source of bioactive compounds. The main aim of this review is to highlight the nucleic acid sequencing experimental strategies used in halophile studies in concert with the presentation of recent examples of bioproducts and functions discovered in silico in the halophile's genomes. We point out methodological gaps and solutions based on in silico methods that are helpful in the identification of valuable bioproducts synthesized by halophiles. We also show the potential of an increasing number of publicly available genomic and metagenomic data for halophilic organisms that can be analysed to identify such new bioproducts and their producers.

Efficient removal of various textile dyes from wastewater by novel thermo-halotolerant laccase

A novel thermostable/halotolerant metagenome-derived laccase (PersiLac2) from tannery wastewater was purified to remove textile dyes in this study. The enzyme was highly active over a wide temperature and pH range and maintained 73.35% of its initial activity after 30 days, at 50 °C. The effect of various metal and organic-solvent tolerance on PersiLac2 showed, retaining greater than 53% activity at 800 mM of metal ions, 52.12% activity at 6 M NaCl, and greater than 44.09% activity at 20% organic solvents. PersiLac2 manifested effective removal of eight different textile dyes from azo, anthraquinone, and triphenylmethane families. It decolorized 500 mg/L of Alizarin yellow, Carmine, Congo red and Bromothymol blue with 99.74-55.85% efficiency after 15 min, at 50 °C, without mediator. This enzyme could practically remove dyes from a real textile effluent and it displayed significant detoxification in rice seed germination tests. In conclusion, PersiLac2 could be useful in future for decolorization/detoxification of wastewater.

Thermostable cellulose saccharifying microbial enzymes: Characteristics, recent advances and biotechnological applications

Cellulases play a promising role in the bioconversion of renewable lignocellulosic biomass into fermentable sugars which are subsequently fermented to biofuels and other value-added chemicals. Besides biofuel industries, they are also in huge demand in textile, detergent, and paper and pulp industries. Low titres of cellulase production and processing are the main issues that contribute to high enzyme cost. The success of ethanol-based biorefinery depends on high production titres and the catalytic efficiency of cellulases functional at elevated temperatures with acid/alkali tolerance and the low cost. In view of their wider application in various industrial processes, stable cellulases that are active at elevated temperatures in the acidic-alkaline pH ranges, and organic solvents and salt tolerance would be useful. This review provides a recent update on the advances made in thermostable cellulases. Developments in their sources, characteristics and mechanisms are updated. Various methods such as rational design, directed evolution, synthetic & system biology and immobilization techniques adopted in evolving cellulases with ameliorated thermostability and characteristics are also discussed. The wide range of applications of thermostable cellulases in various industrial sectors is described.