OBJECTIVES: This study investigated the effects of a polycaprolactone (PCL)-polyethylene glycol (PEG) scaffold incorporated with concentrated growth factor (CGF) on the adhesion, proliferation, and osteogenic differentiation of human periodontal ligament stem cells (hPDLSCs). METHODS: The PCL-PEG-CGF composite scaffold was fabricated using an immersion and freeze-drying technique. Its microstructure, mechanical properties, and biocompatibility were systematically characterized. The hPDLSCs were isolated through enzymatic digestion, and the hPDLSCs were identified through flow cytometry. Third-passage hPDLSCs were seeded onto the composite scaffolds, and their adhesion, proliferation and osteogenic differentiation were assessed using CCK-8 assays, 4',6-diamidino-2-phenylindole (DAPI) staining, alkaline phosphatase (ALP) staining, alizarin red staining, and Western blot analysis of osteogenesis-related proteins [Runt-related transcription factor 2 (Runx2), ALP, and morphogenetic protein 2 (BMP2)]. RESULTS: Scanning electron microscopy revealed that the PCL-PEG-CGF composite scaffold exhibited a honeycomb-like structure with heterogeneous pore sizes. The composite scaffold exhibited excellent hydrophilicity, as evidenced by a contact angle (θ) approaching 0° within 6 s. Its elastic modulus was measured at (4.590 0±0.149 3) MPa, with comparable hydrophilicity, fracture tensile strength, and fracture elongation to PCL-PEG scaffold. The hPDLSCs exhibited significantly improved adhesion to the PCL-PEG-CGF composite scaffold compared with the PCL-PEG scaffold (P<0.01). Additionally, cell proliferation was markedly improved in all the experimental groups on days 3, 5, and 7 (P<0.01), and statistically significant differences were found between the PCL-PEG-CGF group and other groups (P<0.01). The PCL-PEG-CGF group showed significantly elevated ALP activity (P<0.05), increased mineralization nodule formation, and upregulated expression of osteogenic-related proteins (Runx2, BMP2 and ALP; P<0.05). CONCLUSIONS The PCL-PEG-CGF composite scaffold exhibited excellent mechanical properties and biocompatibility, enhancing the adhesion and proliferation of hPDLSCs and promoting their osteogenic differentiation by upregulating osteogenic-related proteins.
Humans ; Polyesters/chemistry* ; Periodontal Ligament/cytology* ; Polyethylene Glycols/chemistry* ; Stem Cells/cytology* ; Tissue Scaffolds ; Cell Proliferation ; Osteogenesis ; Cell Differentiation ; Cell Adhesion ; Bone Morphogenetic Protein 2/metabolism* ; Cells, Cultured ; Alkaline Phosphatase/metabolism* ; Core Binding Factor Alpha 1 Subunit/metabolism* ; Intercellular Signaling Peptides and Proteins/pharmacology* ; Tissue Engineering/methods*

This article reviews the review articles and research papers related to biomanufacturing driven by engineered organisms published in the Chinese Journal of Biotechnology from 2023 to 2024. The content covers 26 aspects, including chassis cells; gene (genome) editing; facilities, tools and methods; biosensors; protein design and engineering; peptides and proteins; screening, expression, characterization and modification of enzymes; biocatalysis; bioactive substances; plant natural products; microbial natural products; development of microbial resources and biopesticides; steroidal compounds; amino acids and their derivatives; vitamins and their derivatives; nucleosides; sugars, sugar alcohols, oligosaccharides, polysaccharides and glycolipids; organic acids and monomers of bio-based materials; biodegradation of polymeric materials and biodegradable materials; intestinal microorganisms, live bacterial drugs and synthetic microbiomes; microbial stress resistance engineering; biodegradation and conversion utilization of lignocellulose; C1 biotechnology; bioelectron transfer and biooxidation-reduction; biotechnological environmental protection; risks and regulation of biomanufacturing driven by engineered organisms, with hundreds of technologies and products commented. It is expected to provide a reference for readers to understand the latest progress in research, development and commercialization related to biomanufacturing driven by engineered organisms.
Biotechnology/methods* ; Gene Editing ; Genetic Engineering ; Metabolic Engineering ; Protein Engineering ; Biosensing Techniques

As the chip of synthetic biology, enzymes play a vital role in the bio-manufacturing industry. The development of diverse functional enzymes can provide a rich toolbox for the development of synthetic biology. This article reports the construction of an artificial enzyme with the introduction of a non-natural cofactor. By introducing the 4-dimethylaminopyridine (DMAP) cofactor into the optimal protein skeleton via covalent bonds based on a click-chemistry strategy, we successfully constructed a novel artificial enzyme with the DMAP cofactor as the catalytic center. The artificial enzyme successfully catalyzed an unnatural asymmetric Morita-Baylis- Hillman (MBH) reaction between cycloketenone and p-nitrobenzaldehyde, with a conversion rate of 90% and enantioselectivity (e.e.) of 38%. This study not only provides an effective strategy for the design of new artificial enzymes but also establishes a theoretical basis for the development of unnatural biocatalytic MBH reactions.
Biocatalysis ; 4-Aminopyridine/chemistry* ; Enzymes/metabolism* ; Coenzymes/chemistry* ; Benzaldehydes/chemistry* ; Protein Engineering/methods* ; Click Chemistry

Predicting protein mutation effects is a key challenge in bioinformatics and protein engineering. Recent advancements in deep learning, particularly the development of protein language models (PLMs), have brought new opportunities to this field. This review summarizes the application of PLMs in predicting protein mutation effects, focusing on three main types of models: sequence-based models, structure-based models, and models that combine sequence and structural information. We analyze in detail the principles, advantages, and limitations of these models and discuss the application of unsupervised and supervised learning in model training. Furthermore, this paper discusses the main challenges currently faced, including the acquisition of high-quality datasets and the handling of data noise. Finally, we look ahead to future research directions, including the application prospects of emerging technologies such as multimodal fusion and few-shot learning. This review aims to provide researchers with a comprehensive perspective to further advance the prediction of protein mutation effects.
Mutation ; Proteins/chemistry* ; Computational Biology/methods* ; Deep Learning ; Protein Engineering

Natural components serve the survival instincts of cells that are obtained through long-term evolution, while they often fail to meet the demands of engineered cells for efficiently performing biological functions in special industrial environments. Enzymes, as biological catalysts, play a key role in biosynthetic pathways, significantly enhancing the rate and selectivity of biochemical reactions. However, the catalytic efficiency, stability, substrate specificity, and tolerance of natural enzymes often fall short of industrial production requirements. Therefore, exploring and modifying enzymes to suit specific biomanufacturing processes has become crucial. In recent years, artificial intelligence (AI) has played an increasingly important role in the discovery, evaluation, engineering, and de novo design of proteins. AI can accelerate the discovery and optimization of proteins by analyzing large amounts of bioinformatics data and predicting protein functions and characteristics by machine learning and deep learning algorithms. Moreover, AI can assist researchers in designing new protein structures by simulating and predicting their performance under different conditions, providing guidance for protein design. This paper reviews the latest research advances in protein discovery, evaluation, engineering, and de novo design for biomanufacturing and explores the hot topics, challenges, and emerging technical methods in this field, aiming to provide guidance and inspiration for researchers in related fields.
Protein Engineering/methods* ; Artificial Intelligence ; Proteins/genetics* ; Computational Biology ; Machine Learning ; Data Mining ; Algorithms ; Deep Learning

Transcription factor (TF)-based biosensors have been widely applied in metabolic engineering, synthetic biology, metabolites monitoring, etc. These biosensors are praised for the high orthogonality, modularity, and operability. However, most natural TFs with weak responses and low specificity still demand optimization for desired performance in applications. Herein, we comprehensively summarize the recent advances in the engineering and optimization of TF-based biosensors with the assistance of computational simulation and artificial intelligence. This review includes the regulatory protein engineering aided by protein structure prediction and ligand binding simulation and the regulatory protein responses predicted by a mathematical model obtained from machine learning of mutagenesis data. In comparison with conventional tools, computational simulation and artificial intelligence enable more accurate and rapid design and construction of biosensors. Thus, these technologies will greatly promote the development of novel biosensors for applications.
Biosensing Techniques/methods* ; Transcription Factors/metabolism* ; Artificial Intelligence ; Protein Engineering/methods* ; Computer Simulation ; Synthetic Biology ; Machine Learning

Collagen, a vital matrix protein for various tissue and functions in animals, is widely applied in biomaterials. In type Ⅰ collagen, missense mutations of glycine (Gly) in the Gly-Xaa-Yaa triplet of the triple helix are a major cause of osteogenesis imperfecta (OI). Clinical manifestations exhibit marked heterogeneity, spanning a broad disease spectrum from mild skeletal fragility (Type Ⅰ) to severe limb deformities (Type Ⅲ) and perinatal lethal forms (Type Ⅱ). This study utilized recombinant collagen as a model to further elucidate whether Gly→Ala/Val mutations at the N-terminus of the integrin-binding sequence GFPGER affect collagen structure and function, and to explore the underlying mechanisms by which missense mutations impact the biological function of collagen. By introducing Ala and Val substitutions at seven Gly positions N-terminal to the GFPGER sequence, we systematically assessed the effects of these amino acid replacements on the triple-helical structure, thermal stability, integrin-binding ability, and cell adhesion of recombinant collagen. All constructs formed a stable triple-helix structure, with slightly compromised thermal stability. Gly→Val substitutions increased the susceptibility of recombinant collagen to trypsin, which suggested local conformational perturbations in the triple helix. In addition, Gly→Val substitutions significantly reduced the integrin-binding affinity and decreased HT1080 cell adhesion, with the effects stronger than Gly→Ala substitutions. Compared with Gly→Ala substitutions, substitution of Gly with the larger residue Val had enhanced negative effects on the structure and function of recombinant collagen. These findings provide new insights into the molecular mechanisms of osteogenesis imperfecta and offer theoretical references and experimental foundations for the design of collagen sequences and the development of collagen-based biomaterials.
Recombinant Proteins/biosynthesis* ; Glycine/genetics* ; Humans ; Osteogenesis Imperfecta/genetics* ; Integrins/metabolism* ; Collagen/metabolism* ; Collagen Type I/metabolism* ; Amino Acid Substitution ; Mutation ; Mutation, Missense

Protein Engineering and Enzyme Engineering is a professional core course for life science-related majors in higher education, aiming to help students apply theoretical knowledge to engineering practice. The rapid development of biomanufacturing has placed new demands on the training of protein and enzyme engineers. However, due to the complexity and strong interdisciplinary nature of the course contents, traditional offline teaching modes have poor teaching performance and are unable to meet the era's demand for high-tech innovative talents in the field of biomanufacturing. To solve the above problems, we carried out the exploration and practice of blended teaching in Protein Engineering and Enzyme Engineering. We designed the teaching philosophy characterized by collaboration of online and offline learning, integration of pre-class preparation, in-class learning, and post-class review, linkage of online platforms, learning resources, teachers, and students, and enhancement via scientific research, scientific contests, course experiments, production practice, and lesson learning. We developed a three-phase (pre-class, in-class, and post-class) teaching program and established a two-level (online-offline) teaching evaluation mechanism. This teaching mode upholds the student-oriented concept, strengthens the deep integration of theory and practice, and focuses on cultivating students' innovative thinking and practical ability. The practical results show that this teaching mode can improve the teaching quality of Protein Engineering and Enzyme Engineering, enhance students' scientific and technological innovation ability, and meet the national demand for biomanufacturing talents.
Protein Engineering ; Teaching ; Enzymes/genetics* ; Humans

Phenylpyruvic acid (PPA) is used as a food and feed additive and has a wide range of applications in the pharmaceutical, chemical and other fields. At present, PPA is mainly produced by chemical synthesis. With the green transformation of the manufacturing industry, biotransformation will be a good alternative for PPA production. The L-amino acid deaminase (PmiLAAD) from Proteus mirabilis has been widely studied for the production of PPA. However, the low yield limits its industrial production. To further enhance the production of PPA and better meet industrial demands, a more efficient synthesis method for PPA was established. In this study, PmiLAAD was heterologously expressed in Escherichia coli. Subsequently, a colorimetric reaction method was established to screen the strains with high PPA production. The semi-rational design of PmiLAAD was carried out, and the obtained triple-site mutant V18 (V437I/S93C/E417A) showed a 35% increase in catalytic activity compared with the wild type. Meanwhile, the effect of N-terminal truncation on the catalytic activity of the V18 mutant was investigated. After the optimization of the whole-cell conditions for the obtained mutant V18-N7, fed-batch conversion was carried out in a 5-L fermenter, and 44.13 g/L of PPA was synthesized with a conversion rate of 88%, which showed certain potential for industrial application. This study lays foundation for the industrial production of phenylpyruvic acid and also offers insights into the biosynthesis of other chemicals.
Escherichia coli/metabolism* ; Proteus mirabilis/genetics* ; Phenylpyruvic Acids/metabolism* ; Protein Engineering/methods* ; Recombinant Proteins/biosynthesis* ; Bacterial Proteins/metabolism*

Penicillin G acylases (PGAs) are industrially important enzymes primarily used for the synthesis of first- and second-generation cephalosporins or penicillins. This study aims to establish a high-efficiency biosynthetic system for cefradine on the purpose of significantly enhancing its catalytic efficiency in cefradine synthesis and developing its potentials for industrial application. In this study, we identified and engineered penicillin G acylase and obtained a highly active mutant KsPGA M7(M168F/F313G) for the synthesis of cefradine. The mutant achieved a conversion rate over 95% in the scaled-up reaction. To validate its industrial applicability, we immobilized both the wild-type and mutant enzymes and applied them in continuous flow reactions, which achieved a space-time yield of 2 800 g/(L·d). This study lays a foundation for the future applications of penicillin G acylases in the industrial synthesis of cefradine.
Penicillin Amidase/biosynthesis* ; Protein Engineering/methods* ; Cephradine/metabolism* ; Escherichia coli/metabolism* ; Enzymes, Immobilized/metabolism* ; Recombinant Proteins/biosynthesis*