OBJECTIVE: To identify the role of gene silencing or overexpression of Nemo-like kinase (NLK) during the process of neural differentiation of human mesenchymal stem cells (hBMSCs), and to explore the effect of NLK downregulation by transfection of small interfering RNA (siRNA) on promoting neuralized tissue engineered bone regeneration. METHODS: NLK-knockdown hBMSCs were established by transfection of siRNA (the experimental group was transfected with siRNA silencing the NLK gene, the control group was transfected with control siRNA and labeled as negative control group), and NLK-overexpression hBMSCs were established using lentivirus vector transfection technique (the experimental group was infected with lentivirus overexpressing the NLK gene, the control group was infected with an empty vector lentivirus and labeled as the empty vector group). After neurogenic induction, quantitative real-time polymerase chain reaction (qPCR) was used to detect the expression of neural-related gene, and Western blot as well as immunofluorescence staining about several specific neural markers were used to evaluate the neural differentiation ability of hBMSCs.6-week-old male nude mice were divided into 4 groups: ① β-tricalcium phosphate (β-TCP) group, ② β-TCP+ osteogenic induced hBMSCs group, ③ β-TCP+ siRNA-negative control (siRNA-NC) transfection hBMSCs group, ④ β-TCP+ siRNA-NLK transfection hBMSCs group. Four weeks after the subcutaneous ectopic osteogenesis models were established, the osteogenesis and neurogenesis were detected by hematoxylin-eosin (HE) staining, Masson staining and tissue immunofluorescence assay. Statistical analysis was conducted by independent sample t test. RESULTS: After gene silencing of NLK by siRNA in hBMSCs, neural-related genes, including the class Ⅲ β-tubulin (TUBB3), microtubule association protein-2 (MAP2), soluble protein-100 (S100), nestin (NES), NG2 proteoglycan (NG2) and calcitonin gene-related peptide (CGRP), were increased significantly in NLK-knockdown hBMSCs compared with the negative control group(P < 0.05), and the expression levels of TUBB3 and MAP2 of the NLK silencing group were also increased. Oppositely, after NLK was overexpressed using lentivirus vector transfection technique, TUBB3, MAP2, S100 and NG2 were significantly decreased in NLK-overexpression hBMSCs compared with the empty vector group (P < 0.05), and the expression level of TUBB3 was also decreased. 4 weeks after the subcutaneous ectopic osteogenesis model was established, more mineralized tissues were formed in the β-TCP+ siRNA-NLK transfection hBMSCs group compared with the other three groups, and the expression of BMP2 and S100 was higher in the β-TCP+ siRNA-NLK transfection hBMSCs group than in the other groups. CONCLUSION Gene silencing of NLK by siRNA promoted the ability of neural differentiation of hBMSCs in vitro and promoted neuralized tissue engineered bone formation in subcutaneous ectopic osteogenic models in vivo in nude mice.
Bone Regeneration/genetics* ; Animals ; Mesenchymal Stem Cells/cytology* ; Humans ; RNA, Small Interfering/genetics* ; Tissue Engineering/methods* ; Cell Differentiation ; Mice, Nude ; Gene Silencing ; Mice ; Male ; Protein Serine-Threonine Kinases/genetics* ; Intracellular Signaling Peptides and Proteins/genetics* ; Transfection ; Cells, Cultured ; Lentivirus/genetics*

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

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*

Proteins with specific functions and characteristics play a crucial role in biomedicine and nanotechnology. De novo protein design enables the customization of sequences to produce proteins with desired structures that do not exist in the nature. In recent years, with the rapid development of artificial intelligence (AI), deep learning-based generative models have increasingly become powerful tools, enabling the design of functional proteins with atomic-level precision. This article provides an overview of the evolution of de novo protein design, with focus on the latest algorithmic models, and then analyzes existing challenges such as low design success rates, insufficient accuracy, and dependence on experimental validation. Furthermore, this article discusses the future trends in protein design, aiming to provide insights for researchers and practitioners in this field.
Artificial Intelligence ; Proteins/chemistry* ; Algorithms ; Protein Engineering/methods* ; Deep Learning