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[前沿资讯 ] Illinois researchers develop next-generation organic nanozymes and point-of-use system for food and agricultural uses 进入全文
EurekAlert
Nanozymes are synthetic materials that have enzyme-like catalytic properties, and they are broadly used for biomedical purposes, such as disease diagnostics. However, inorganic nanozymes are generally toxic, expensive, and complicated to produce, making them unsuitable for the agricultural and food industries. A University of Illinois Urbana-Champaign research team has developed organic-material-based nanozymes that are non-toxic, environmentally friendly, and cost effective. In two new studies, they introduce next-generation organic nanozymes and explore a point-of-use platform for molecule detection in agricultural products.“The first generation of organic-compound-based (OC) nanozymes had some minor drawbacks, so our research group worked to make improvements. The previous OC nanozymes required the use of particle stabilizing polymers having repeatable functional groups, which assured stability of the nanozyme’s nanoscale framework, but didn’t achieve a sufficiently small particle size,” said lead author Dong Hoon Lee, who completed his Ph.D. from the Department of Agricultural and Biological Engineering (ABE), part of the College of Agricultural, Consumer and Environmental Sciences and The Grainger College of Engineering at the U. of I. In the new iteration, they used a core amino acid (L- alanine) and polyethylene glycol as constituent materials and a novel particle synthesis technique that allowed them to bring the particle size down to less than 100 nanometers. This nanozyme resembles the physical framework and mimics the catalytic activity of target enzymes.
[学术文献 ] Computational design of serine hydrolases 进入全文
Science
The design of enzymes with complex active sites that mediate multistep reactions remains an outstanding challenge. With serine hydrolases as a model system, we combined the generative capabilities of RFdiffusion with an ensemble generation method for assessing active site preorganization to design enzymes starting from minimal active site descriptions. Experimental characterization revealed catalytic efficiencies (kcat/Km) up to 2.2x105 M−1 s−1 and crystal structures that closely match the design models (Cα RMSDs < 1 Å). Selection for structural compatibility across the reaction coordinate enabled identification of new catalysts in low-throughput screens with five different folds distinct from those of natural serine hydrolases. Our de novo approach provides insight into the geometric basis of catalysis and a roadmap for designing enzymes that catalyze multistep transformations.
[学术文献 ] A metagenomic ‘dark matter’ enzyme catalyses oxidative cellulose conversion 进入全文
Nature
The breakdown of cellulose is one of the most important reactions in nature1,2 and is central to biomass conversion to fuels and chemicals3. However, the microfibrillar organization of cellulose and its complex interactions with other components of the plant cell wall poses a major challenge for enzymatic conversion4. Here, by mining the metagenomic ‘dark matter’ (unclassified DNA with unknown function) of a microbial community specialized in lignocellulose degradation, we discovered a metalloenzyme that oxidatively cleaves cellulose. This metalloenzyme acts on cellulose through an exo-type mechanism with C1 regioselectivity, resulting exclusively in cellobionic acid as a product. The crystal structure reveals a catalytic copper buried in a compact jelly-roll scaffold that features a flattened cellulose binding site. This metalloenzyme exhibits a homodimeric configuration that enables in situ hydrogen peroxide generation by one subunit while the other is productively interacting with cellulose. The secretome of an engineered strain of the fungus Trichoderma reesei expressing this metalloenzyme boosted the glucose release from pretreated lignocellulosic biomass under industrially relevant conditions, demonstrating its biotechnological potential. This discovery modifies the current understanding of bacterial redox enzymatic systems devoted to overcoming biomass recalcitrance5,6,7. Furthermore, it enables the conversion of agro-industrial residues into value-added bioproducts, thereby contributing to the transition to a sustainable and bio-based economy.
[学术文献 ] Rational multienzyme architecture design with iMARS 进入全文
Cell
Biocatalytic cascades with spatial proximity can orchestrate multistep pathways to form metabolic highways, which enhance the overall catalytic efficiency. However, the effect of spatial organization on catalytic activity is poorly understood, and multienzyme architectural engineering with predictable performance remains unrealized. Here, we developed a standardized framework, called iMARS, to rapidly design the optimal multienzyme architecture by integrating high-throughput activity tests and structural analysis. The approach showed potential for industrial-scale applications, with artificial fusion enzymes designed by iMARS significantly improving the production of resveratrol by 45.1-fold and raspberry ketone by 11.3-fold in vivo, as well as enhancing ergothioneine synthesis in fed-batch fermentation. In addition, iMARS greatly enhanced the in vitro catalytic efficiency of the multienzyme complexes for PET plastic depolymerization and vanillin biosynthesis. As a generalizable and flexible strategy at molecular level, iMARS could greatly facilitate green chemistry, synthetic biology, and biomanufacturing.
[前沿资讯 ] 新型工程酶变体可拓展环亚胺酸结构多样性 进入全文
科学网
中国科学院上海药物研究所研究员廖苍松课题组与中国科学院天津工业生物技术研究所研究员盛翔课题组合作，利用聚焦理性迭代位点特异性突变（FRISM）策略，对脱羧醛缩酶UstD进行了半理性工程改造，调控了UstD对邻位二酮亲电试剂的区域选择性和立体选择性，获得的工程酶变体具有极佳的选择性和较广的底物谱，拓展了环亚胺酸的结构多样性。1月15日，相关研究在线发表于《德国应用化学》。研究团队使用FRISM的半理性策略开展酶工程改造研究，通过酶变体设计探索底物结合口袋的残基对选择性的调控机制。经过三轮突变后得到的UstD2.0AAM对产物1c的转化率为46%，选择性为90%，对产物2b的转化率为72%，选择性为94%。研究人员解析了2b的立体构型，并高选择性地合成了共计30种在α和γ位置具有立体中心的环状亚胺酸，实现了百毫克规模的制备反应，收率达89%。分子动力学模拟研究表明，ApUstD和UstD2.0AAM两种酶活性位点空腔大小和疏水性存在明显差异，这些差异导致了底物在口袋内具有不同的结合构象，也是不同酶表现出不同反应选择性的根本原因。
[学术文献 ] Multi-Modal CLIP-Informed Protein Editing 进入全文
Health Data Science
Background: Proteins govern most biological functions essential for life, and achieving controllable protein editing has made great advances in probing natural systems, creating therapeutic conjugates, and generating novel protein constructs. Recently, machine learning-assisted protein editing (MLPE) has shown promise in accelerating optimization cycles and reducing experimental workloads. However, current methods struggle with the vast combinatorial space of potential protein edits and cannot explicitly conduct protein editing using biotext instructions, limiting their interactivity with human feedback. Methods: To fill these gaps, we propose a novel method called ProtET for efficient CLIP-informed protein editing through multi-modality learning. Our approach comprises 2 stages: In the pretraining stage, contrastive learning aligns protein–biotext representations encoded by 2 large language models (LLMs). Subsequently, during the protein editing stage, the fused features from editing instruction texts and original protein sequences serve as the final editing condition for generating target protein sequences. Results: Comprehensive experiments demonstrated the superiority of ProtET in editing proteins to enhance human-expected functionality across multiple attribute domains, including enzyme catalytic activity, protein stability, and antibody-specific binding ability. ProtET improves the state-of-the-art results by a large margin, leading to substantial stability improvements of 16.67% and 16.90%. Conclusions: This capability positions ProtET to advance real-world artificial protein editing, potentially addressing unmet academic, industrial, and clinical needs.
