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[学术文献 ] Value-added biotransformation of agricultural byproducts by cellulolytic fungi: a review 进入全文
ENVIRONMENTAL SCIENCE & TECHNOLOGY
Agricultural byproducts generally contain abundant bioactive compounds (e.g., cellulose/hemicellulose, phenolic compounds (PCs), and dietary fibers (DFs)), but most of them are neglected and underutilized. Owing to the complicated and rigid structures of agricultural byproducts, a considerable amount of bioactive compounds are entrapped in the polymer matrix, impeding their further development and utilization. In recent years, the prominent performance of cellulolytic fungi to grow and degrade agricultural byproducts has been applied to achieve efficient biotransformation of byproducts to high-value compounds, which is a green and sustainable strategy for the reutilization of agricultural byproducts. This review comprehensively summarizes recent progress in the value-added biotransformation of agricultural byproducts by cellulolytic fungi, including (1) direct utilization of agricultural byproducts for biochemicals and bioethanol production via a consolidated bioprocessing, (2) recovery and biotransformation of bounded PCs from agricultural byproducts for higher bioactive properties, as well as (3) modification and conversion of insoluble DF from agricultural byproducts to produce functional soluble DF. The functional enzymes, potential mechanisms, and metabolic pathways involved are emphasized. Moreover, promising advantages and current bottlenecks using cellulolytic fungi have also been elucidated, shedding further perspectives for sustainable and efficient reutilization of agricultural byproducts by cellulolytic fungi.
[学术文献 ] Network for knowledge Organization (NEKO): An AI knowledge mining workflow for synthetic biology research 进入全文
Metabolic Engineering
Large language models (LLMs) can complete general scientific question-and-answer, yet they are constrained by their pretraining cut-off dates and lack the ability to provide specific, cited scientific knowledge. Here, we introduce Network for Knowledge Organization (NEKO), a workflow that uses LLM Qwen to extract knowledge through scientific literature text mining. When user inputs a keyword of interest, NEKO can generate knowledge graphs to link bioinformation entities and produce comprehensive summaries from PubMed search. NEKO significantly enhance LLM ability and has immediate applications in daily academic tasks such as education of young scientists, literature review, paper writing, experiment planning/troubleshooting, and new ideas/hypothesis generation. We exemplified this workflow's applicability through several case studies on yeast fermentation and cyanobacterial biorefinery. NEKO's output is more informative, specific, and actionable than GPT-4's zero-shot Q&A. NEKO offers flexible, lightweight local deployment options. NEKO democratizes artificial intelligence (AI) tools, making scientific foundation model more accessible to researchers without excessive computational power.
[学术文献 ] Deep learning for NADNADP cofactor prediction and engineering using transformer attention analysis in enzymes 进入全文
Metabolic Engineering
Understanding and manipulating the cofactor preferences of NAD(P)-dependent oxidoreductases, the most widely distributed enzyme group in nature, is increasingly crucial in bioengineering. However, large-scale identification of the cofactor preferences and the design of mutants to switch cofactor specificity remain as complex tasks. Here, we introduce DISCODE (Deep learning-based Iterative pipeline to analyze Specificity of COfactors and to Design Enzyme), a novel transformer-based deep learning model to predict NAD(P) cofactor preferences. For model training, a total of 7,132 NAD(P)-dependent enzyme sequences were collected. Leveraging whole-length sequence information, DISCODE classifies the cofactor preferences of NAD(P)-dependent oxidoreductase protein sequences without structural or taxonomic limitation. The model showed 97.4% and 97.3% of accuracy and F1 score, respectively. A notable feature of DISCODE is the interpretability of its transformer layers. Analysis of attention layers in the model enables identification of several residues that showed significantly higher attention weights. They were well aligned with structurally important residues that closely interact with NAD(P), facilitating the identification of key residues for determining cofactor specificities. These key residues showed high consistency with verified cofactor switching mutants. Integrated into an enzyme design pipeline, DISCODE coupled with attention analysis, enables a fully automated approach to redesign cofactor specificity.
[前沿资讯 ] Argonne team breaks new ground in AI-driven protein design 进入全文
Eurekalert
Harnessing the power of artificial intelligence (AI) and the world’s fastest supercomputers, a research team led by the U.S. Department of Energy’s (DOE) Argonne National Laboratory has developed an innovative computing framework to speed up the design of new proteins. One of the key innovations of the team’s MProt-DPO framework is its ability to integrate different types of data streams, or “multimodal data.” It combines traditional protein sequence data with experimental results, molecular simulations and even text-based narratives that provide detailed insights into each protein’s properties. This approach has the potential to accelerate protein discovery for a wide range of applications.
[学术文献 ] Computational Design-Enabled Divergent Modification of Monoterpene Synthases for Terpenoid Hyperproduction 进入全文
ACS CATALYSIS
Enzymes’catalytic promiscuity enables the alteration of product specificity via protein engineering; yet, harnessing this promiscuity to achieve desired catalytic reactions remains challenging. Here, we identified HCinS, a monoterpene synthase (MTPS) with a high efficiency and specificity for 1,8-cineole biosynthesis. Quantum mechanics/molecular mechanics (QM/MM) simulations, which were performed based on the resolved crystal structure of HCinS, revealed the mechanistic details of the biosynthetic cascade reactions. Guided by these insights, in silico HCinS variants were designed with fine-tuned transition-state energies and reaction microenvironments. Three variants (T111A, N135H, F236M), each with one amino acid substitution, exhibited high specificity in the production of monocyclic (R)-α-terpineol, (R)-limonene, and acyclic myrcene, respectively, maintaining over 55% efficiency of native HCinS. These designed HCinS variants surpassed naturally evolved isozymes in catalytic capacity and enabled yeast to achieve the highest microbial titer of each corresponding terpene. Furthermore, the single mutation of four functional equivalent amino acids in other four identified TPSs, respectively, resulted in the expected shifts on product specificity as HCinS variants. This research offers insights into the mechanisms controlling the TPS’s product promiscuity and highlights the universal applicability of computational design in reshaping the product specificity of TPSs, thereby paving innovative avenues for creating enzymes with applications in chemistry and synthetic biology.
[学术文献 ] Enhancing Manganese Peroxidase Innovations in Genetic Modification, Screening Processes, and Sustainable Agricultural Applications 进入全文
Journal of Agricultural and Food Chemistry
Manganese peroxidase (MnP), a vital extracellular enzyme for the degradation of lignin and other organic pollutants, has demonstrated immense potential for agricultural and environmental applications, including straw pretreatment, feed fermentation, mycotoxin degradation, and water treatment. However, current research remains in its exploratory phase, with naturally sourced MnP unable to meet industrial-scale demands and no mature commercial enzyme preparations available on the market. This comprehensive review innovatively constructs a framework for MnP research, probing into its molecular conformation and catalytic principles, while providing an overview of the advancements in high-throughput screening and In silco designing strategies. Specifically, this review focuses on the practical applications of MnP in sustainable agriculture, elaborating on its potential and challenges in straw resource utilization, efficient feed fermentation, mycotoxin control, and water quality improvement. Furthermore, this review summarizes the recent achievements in optimizing MnP activity through enzyme engineering techniques and discuss customized mutation strategies tailored to specific agricultural and environmental requirements, thereby laying a solid theoretical foundation and scientific basis for the industrial production and commercialization of MnP.
