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In the present study, a pyridoxal-5'-phosphate (PLP)-dependent L-aspartate-α-decarboxylase from (TcPanD) was selected for protein engineering to efficiently produce β-alanine. A mutant PanD-R98H/K305S with a 2.45-fold higher activity than the wide type was selected through error-prone PCR, site-saturation mutagenesis, and 96-well plate screening technologies. The characterization of purified enzyme TcPanD-R98H/K305S showed that the optimal cofactor PLP concentration, temperature, and pH were 0.04% (), 50 °C, and 7.0, respectively. The 1mM of Na, Ni, Co, K, and Ca stimulated the activity of TcPanD-R98H/K305S, while only 5 mM of Ni and Na could increase its activity. The kinetic analysis indicated that TcPanD-R98H/K305S had a higher substrate affinity and enzymatic reaction rate than the wild enzyme. A total of 267 g/L substrate l-aspartic acid was consumed and 170.5 g/L of β-alanine with a molar conversion of 95.5% was obtained under the optimal condition and 5-L reactor fermentation.
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http://dx.doi.org/10.3390/molecules25061280 | DOI Listing |
New Phytol
September 2025
State Key Laboratory for Quality and Safety of Agro-Products, Key Laboratory of Biotechnology in Plant Protection of MARA, Zhejiang Key Laboratory of Green Plant Protection, Institute of Plant Virology, Ningbo University, Ningbo, 315211, China.
Our previous work identified p3-interacting protein (P3IP) as a novel plant factor that interacts with rice stripe virus p3 protein and activates autophagy to mediate its degradation, thereby restricting infection. However, the mechanism of P3IP-mediated autophagy and the evolutionary conservation of its antiviral function remain unknown. This study demonstrates that two Arabidopsis thaliana homologs, AtP3IP and AtP3IPH (Arabidopsis P3IP homologs, AtP3IPs), similarly activate autophagy and confer resistance to turnip mosaic virus (TuMV).
View Article and Find Full Text PDFiScience
September 2025
School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China.
Deep learning has rapidly emerged as a promising toolkit for protein optimization, yet its success remains limited, particularly in the realm of activity. Moreover, most algorithms lack rigorous iterative evaluation, a crucial aspect of protein engineering exemplified by classical directed evolution. This study introduces DeepDE, a robust iterative deep learning-guided algorithm leveraging triple mutants as building blocks and a compact library of ∼1,000 mutants for training.
View Article and Find Full Text PDFBiochem Biophys Rep
December 2025
Department of Neurosurgery, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany.
Brillouin microscopy allows mechanical investigations of biological materials at the subcellular level and can be integrated with Raman spectroscopy for simultaneous chemical mapping, thus enabling a more comprehensive interpretation of biomechanics. The present study investigates different in vitro glioblastoma models using a combination of Brillouin and Raman microspectroscopy. Spheroids of the U87-MG cell line and two patient-derived cell lines as well as patient-derived organoids were used.
View Article and Find Full Text PDFSynth Biol (Oxf)
August 2025
Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, United States.
Modular cloning systems streamline laboratory workflows by consolidating genetic 'parts' into reusable and modular collections, enabling researchers to fast-track strain construction. The GoldenBraid 2.0 modular cloning system utilizes the cutting property of type IIS restriction enzymes to create defined genetic 'grammars', which facilitate the reuse of standardized genetic parts and assembly of genetic parts in the right order.
View Article and Find Full Text PDFChem Sci
August 2025
Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University Shanghai 200240 China
Predicting Antibody-Antigen (Ab-Ag) docking and structure-based design represent significant long-term and therapeutically important challenges in computational biology. We present SAGERank, a general, configurable deep learning framework for antibody design using Graph Sample and Aggregate Networks. SAGERank successfully predicted the majority of epitopes in a cancer target dataset.
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