Pub Date : 2025-11-27DOI: 10.1016/j.bej.2025.110029
Shuai Huang , Haoyuan Tan , Luxuan Sun , Meng Wang , Biqiang Chen
This study demonstrated the advantages of the rotating bed reactor (RBR) over the conventional turbine stirred tank reactor (TSTR) for enzymatically catalyzed biodiesel synthesis using D311-resin immobilized lipase. Integrating simulation and experimental analyses, the work revealed that the rotating bed generated significantly lower shear forces compared to the turbine stirred paddles, thereby preserving lipase integrity and enhancing reusability. Simulations identified tangential velocity—modulated by rotational speed and bed porosity—as the dominant factor governing hydrodynamic velocity and liquid-solid mass transfer coefficients. Experimental validation confirmed these findings: Under optimized conditions, the yield of the fatty acid methyl esters (FAMEs) decreased from 87.49 % to 60.33 % after continuous use of immobilized lipase for 48 cycles in the RBR. In contrast, TSTR systems exhibited accelerated activity loss (≤69.9 % retention after 9 cycles) and yield deterioration (60.3 %). By mitigating shear-induced lipase deactivation and optimizing mass transfer, RBR technology paired with D311-resin immobilized lipase offers a scalable, cost-effective strategy for industrial biodiesel production.
{"title":"Hydrodynamic velocity and interfacial mass transfer dynamics in the rotating bed reactor: Application to enzymatically catalyzed biodiesel production","authors":"Shuai Huang , Haoyuan Tan , Luxuan Sun , Meng Wang , Biqiang Chen","doi":"10.1016/j.bej.2025.110029","DOIUrl":"10.1016/j.bej.2025.110029","url":null,"abstract":"<div><div>This study demonstrated the advantages of the rotating bed reactor (RBR) over the conventional turbine stirred tank reactor (TSTR) for enzymatically catalyzed biodiesel synthesis using D311-resin immobilized lipase. Integrating simulation and experimental analyses, the work revealed that the rotating bed generated significantly lower shear forces compared to the turbine stirred paddles, thereby preserving lipase integrity and enhancing reusability. Simulations identified tangential velocity—modulated by rotational speed and bed porosity—as the dominant factor governing hydrodynamic velocity and liquid-solid mass transfer coefficients. Experimental validation confirmed these findings: Under optimized conditions, the yield of the fatty acid methyl esters (FAMEs) decreased from 87.49 % to 60.33 % after continuous use of immobilized lipase for 48 cycles in the RBR. In contrast, TSTR systems exhibited accelerated activity loss (≤69.9 % retention after 9 cycles) and yield deterioration (60.3 %). By mitigating shear-induced lipase deactivation and optimizing mass transfer, RBR technology paired with D311-resin immobilized lipase offers a scalable, cost-effective strategy for industrial biodiesel production.</div></div>","PeriodicalId":8766,"journal":{"name":"Biochemical Engineering Journal","volume":"227 ","pages":"Article 110029"},"PeriodicalIF":3.7,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-27DOI: 10.1016/j.bej.2025.110030
Yanling Wang , Lei Ma , Wilburt Tam, Weibin Zheng, Dana Lee, Qinhong Yu, Lixin Feng
AntibodyPlus denotes engineered antibodies that incorporate an added functional module, such as an enzyme, cytokine, peptide, or small-molecule payload, to expand or enhance their biological activity beyond that of a conventional antibody. Among its formats, enzyme-fusion antibodies, coupling binding modules to enzymes, enable precise functions for enzyme replacement, prodrug activation, diagnostics, and cancer therapy. However, as a hard-to-express protein, challenges remain in maintaining enzyme integrity, activity, and proper bispecific assembly during its manufacture. Thus, for enzyme-fusion antibodies, optimizing transfection and clone selection strategies that consider both titer and enzymatic activity is key for manufacturing and clinical translation. In this study, a Leap-In transposon site-specific integration strategy was first introduced to the enzyme-fusion antibody CHO transfection and expression system, providing sufficient, high-quality clones for screening. Sequentially, a novel tailored multidimensional single-clone screening method was originally established and introduced for the expression of asymmetric enzyme-fused antibodies, achieving both high titer and high enzyme activity systematically. It enabled efficient identification of high-performing clones, achieving titers above 6 g/L and enzymatic specific activities over 1200 mU/mg through vector chain balancing, optimized screening strategies, and analytical validation such as CE-SDS, SEC-HPLC, and ELISA. This work establishes a robust and resource-efficient CLD workflow for enzyme-fusion antibodies, offering a significant advancement for the expression and development of complex, hard-to-express biologics.
{"title":"AntibodyPlus enzyme fusion protein cell line development using a novel multidimensional screening of productivity, purity, and specific activity","authors":"Yanling Wang , Lei Ma , Wilburt Tam, Weibin Zheng, Dana Lee, Qinhong Yu, Lixin Feng","doi":"10.1016/j.bej.2025.110030","DOIUrl":"10.1016/j.bej.2025.110030","url":null,"abstract":"<div><div>AntibodyPlus denotes engineered antibodies that incorporate an added functional module, such as an enzyme, cytokine, peptide, or small-molecule payload, to expand or enhance their biological activity beyond that of a conventional antibody. Among its formats, enzyme-fusion antibodies, coupling binding modules to enzymes, enable precise functions for enzyme replacement, prodrug activation, diagnostics, and cancer therapy. However, as a hard-to-express protein, challenges remain in maintaining enzyme integrity, activity, and proper bispecific assembly during its manufacture. Thus, for enzyme-fusion antibodies, optimizing transfection and clone selection strategies that consider both titer and enzymatic activity is key for manufacturing and clinical translation. In this study, a Leap-In transposon site-specific integration strategy was first introduced to the enzyme-fusion antibody CHO transfection and expression system, providing sufficient, high-quality clones for screening. Sequentially, a novel tailored multidimensional single-clone screening method was originally established and introduced for the expression of asymmetric enzyme-fused antibodies, achieving both high titer and high enzyme activity systematically. It enabled efficient identification of high-performing clones, achieving titers above 6 g/L and enzymatic specific activities over 1200 mU/mg through vector chain balancing, optimized screening strategies, and analytical validation such as CE-SDS, SEC-HPLC, and ELISA. This work establishes a robust and resource-efficient CLD workflow for enzyme-fusion antibodies, offering a significant advancement for the expression and development of complex, hard-to-express biologics.</div></div>","PeriodicalId":8766,"journal":{"name":"Biochemical Engineering Journal","volume":"227 ","pages":"Article 110030"},"PeriodicalIF":3.7,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-26DOI: 10.1016/j.bej.2025.110026
Guojuan Yi , Zidan Liu , Linyu Luo , Zhiqiang Ding , Tolbert Osire , Mengfei Long , Yongmei Xie
The enhancement of enzyme thermostability is critical for industrial applications such as food processing, and covalent bond engineering, such as disulfide bond formation, has proven effective in achieving this goal. However, the relationship between disulfide bond engineering and internal cavity modulation remained unclear. In this study, we first optimized the thermostability and catalytic performance of pullulanase using computer-aided disulfide bond engineering. The double disulfide bond mutant S643C-E668C/R680C-H715C exhibited significant improvements in thermostability (Tm increased by 6℃, half-life at 70℃ extended by 1.64-fold) and catalytic efficiency (specific activity enhanced by 21.5 %). Molecular dynamics simulations revealed that disulfide bonds stabilized the enzyme structure by reducing conformational flexibility, increasing molecular compactness, and optimizing internal cavities. Building on these findings, we developed a covalent bond “stapling” strategy based on internal cavity engineering and applied it to ethyl carbamate (EC) hydrolase, incorporating both disulfide and non-natural thioether bonds. Specific mutants, such as M46C-K123C and I76C-L212TAG, significantly improved catalytic activity, ethanol tolerance, and thermostability. These enhancements were attributed to rigid connections formed by covalent bonds in critical regions, which mitigated local stress or conformational changes potentially induced by cavity-filling mutations. By elucidating the synergistic effects of disulfide bond engineering and cavity filling, this study provided a novel theoretical and practical foundation for designing industrial enzymes with superior thermostability.
{"title":"Investigating the role of covalent stapling and cavity filling in enzyme thermostability","authors":"Guojuan Yi , Zidan Liu , Linyu Luo , Zhiqiang Ding , Tolbert Osire , Mengfei Long , Yongmei Xie","doi":"10.1016/j.bej.2025.110026","DOIUrl":"10.1016/j.bej.2025.110026","url":null,"abstract":"<div><div>The enhancement of enzyme thermostability is critical for industrial applications such as food processing, and covalent bond engineering, such as disulfide bond formation, has proven effective in achieving this goal. However, the relationship between disulfide bond engineering and internal cavity modulation remained unclear. In this study, we first optimized the thermostability and catalytic performance of pullulanase using computer-aided disulfide bond engineering. The double disulfide bond mutant S643C-E668C/R680C-H715C exhibited significant improvements in thermostability (<em>T</em><sub>m</sub> increased by 6℃, half-life at 70℃ extended by 1.64-fold) and catalytic efficiency (specific activity enhanced by 21.5 %). Molecular dynamics simulations revealed that disulfide bonds stabilized the enzyme structure by reducing conformational flexibility, increasing molecular compactness, and optimizing internal cavities. Building on these findings, we developed a covalent bond “stapling” strategy based on internal cavity engineering and applied it to ethyl carbamate (EC) hydrolase, incorporating both disulfide and non-natural thioether bonds. Specific mutants, such as M46C-K123C and I76C-L212TAG, significantly improved catalytic activity, ethanol tolerance, and thermostability. These enhancements were attributed to rigid connections formed by covalent bonds in critical regions, which mitigated local stress or conformational changes potentially induced by cavity-filling mutations. By elucidating the synergistic effects of disulfide bond engineering and cavity filling, this study provided a novel theoretical and practical foundation for designing industrial enzymes with superior thermostability.</div></div>","PeriodicalId":8766,"journal":{"name":"Biochemical Engineering Journal","volume":"227 ","pages":"Article 110026"},"PeriodicalIF":3.7,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ammonium is abundant in agro-industrial effluents, providing a cost-effective and sustainable nitrogen source for cultivating the cyanobacterium Limnospira fusiformis. However, under high pH conditions, ammonium converts into free ammonia (NH3), causing inhibition. Therefore, an optimal ammonium supply strategy is required. This study assessed L. fusiformis growth under intermittent, continuous, and nighttime ammonium supply methods across four nitrogen loading rates: 3.9, 5.8, 11.6, and 17.4 mg NH₄⁺-N L⁻¹ d⁻¹ . At a loading rate of 11.6 mg NH₄⁺-N L⁻¹ d⁻¹ , intermittent and continuous supply yielded higher productivities (0.22 and 0.20 g L⁻¹ d⁻¹, respectively) than the nighttime method (0.15 g L⁻¹ d⁻¹) (p < 0.05). Increasing the loading rate to 17.4 mg NH₄⁺-N L⁻¹ d⁻¹ resulted in elevated NH₃ concentrations, leading to growth inhibition and productivity declines to 0.09, 0.10, and 0.06 g L⁻¹ d⁻¹ under intermittent, continuous, and nighttime supply, respectively (p < 0.05). This study is the first to demonstrate that continuous or intermittent ammonium supply strategies can be effectively applied in the mass cultivation of L. fusiformis.
农业工业废水中含有丰富的铵，为培养梭状蓝藻提供了一种经济、可持续的氮源。然而，在高pH条件下，铵转化为游离氨（NH3），产生抑制作用。因此，需要一个最佳的铵供应策略。这项研究评估了断断式、连续式和夜间铵供应方式下梭状乳杆菌的生长情况，四种氮负荷率：3.9、5.8、11.6和17.4 mg NH₄+ -N L⁻¹ d⁻¹ 。加载速率为11.6 mg NH₄ ⁺- n L⁻¹ d⁻¹ ,间歇和连续供应产生了更高的生产力(0.22和0.20 g L⁻¹d⁻¹,分别)比夜间的方法(0.15 g L⁻¹d⁻¹)(p & lt; 0.05)。提高加载速率为17.4 mg NH₄⁺- n L⁻¹ d⁻¹ 导致NH₃浓度升高,导致抑制增长和生产率下降至0.09,0.10,和0.06 g L⁻¹ d⁻¹ 在断断续续的,连续的,分别和夜间供应(p & lt; 0.05)。本研究首次证明了连续或间歇铵供应策略可以有效地应用于梭状螺旋藻的大规模种植。
{"title":"Optimizing ammonium supply strategies for mitigation of free ammonia inhibition in the mass cultivation of Limnospira fusiformis","authors":"Haymanot Yenesew Sewunet , Anupreet Kaur Chowdhary , Yuanjun Xia , Mutsumi Sekine , Pranshu Bhatia , Ayirkm Adugna Woldie , Tatsuki Toda","doi":"10.1016/j.bej.2025.110018","DOIUrl":"10.1016/j.bej.2025.110018","url":null,"abstract":"<div><div>Ammonium is abundant in agro-industrial effluents, providing a cost-effective and sustainable nitrogen source for cultivating the cyanobacterium <em>Limnospira fusiformis</em>. However, under high pH conditions, ammonium converts into free ammonia (NH<sub>3</sub>), causing inhibition. Therefore, an optimal ammonium supply strategy is required. This study assessed <em>L. fusiformis</em> growth under intermittent, continuous, and nighttime ammonium supply methods across four nitrogen loading rates: 3.9, 5.8, 11.6, and 17.4 mg NH₄⁺-N L⁻¹ d⁻¹ . At a loading rate of 11.6 mg NH₄⁺-N L⁻¹ d⁻¹ , intermittent and continuous supply yielded higher productivities (0.22 and 0.20 g L⁻¹ d⁻¹, respectively) than the nighttime method (0.15 g L⁻¹ d⁻¹) (<em>p</em> < 0.05). Increasing the loading rate to 17.4 mg NH₄⁺-N L⁻¹ d⁻¹ resulted in elevated NH₃ concentrations, leading to growth inhibition and productivity declines to 0.09, 0.10, and 0.06 g L⁻¹ d⁻¹ under intermittent, continuous, and nighttime supply, respectively (<em>p</em> < 0.05). This study is the first to demonstrate that continuous or intermittent ammonium supply strategies can be effectively applied in the mass cultivation of <em>L. fusiformis</em>.</div></div>","PeriodicalId":8766,"journal":{"name":"Biochemical Engineering Journal","volume":"227 ","pages":"Article 110018"},"PeriodicalIF":3.7,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145682006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-25DOI: 10.1016/j.bej.2025.110017
Pınar Kocabaş
Genome-scale metabolic models (GEMs) are powerful tools for exploring the metabolic state of cells and interpreting complex biological data. A key component of these models is the gene-protein-reaction (GPR) association, which links genes to enzymatic reactions via Boolean logic to account for isozymes and protein complexes. The accuracy of GPRs, alongside the stoichiometric matrix, critically determines the predictive performance of GEMs. GPRs play a central role in constructing condition-specific, cell line, and disease models, and are widely used in gene essentiality analysis, expression profiling, and strain design. This review presents a historical overview of GPR construction in mammalian and yeast GEMs, summarizes the main tools, databases, and methods used for their generation and curation, and identifies current challenges and limitations. Finally, potential improvements in GPR generation frameworks to enhance their utility in systems biology and metabolic engineering applications are discussed.
{"title":"Generation of gene-protein-reaction association rules in genome scale metabolic models: Chronology, challenges, and future perspectives","authors":"Pınar Kocabaş","doi":"10.1016/j.bej.2025.110017","DOIUrl":"10.1016/j.bej.2025.110017","url":null,"abstract":"<div><div>Genome-scale metabolic models (GEMs) are powerful tools for exploring the metabolic state of cells and interpreting complex biological data. A key component of these models is the gene-protein-reaction (GPR) association, which links genes to enzymatic reactions via Boolean logic to account for isozymes and protein complexes. The accuracy of GPRs, alongside the stoichiometric matrix, critically determines the predictive performance of GEMs. GPRs play a central role in constructing condition-specific, cell line, and disease models, and are widely used in gene essentiality analysis, expression profiling, and strain design. This review presents a historical overview of GPR construction in mammalian and yeast GEMs, summarizes the main tools, databases, and methods used for their generation and curation, and identifies current challenges and limitations. Finally, potential improvements in GPR generation frameworks to enhance their utility in systems biology and metabolic engineering applications are discussed.</div></div>","PeriodicalId":8766,"journal":{"name":"Biochemical Engineering Journal","volume":"227 ","pages":"Article 110017"},"PeriodicalIF":3.7,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-24DOI: 10.1016/j.bej.2025.110015
Xiaoxin Du , Lisen Yang , Bo Wang , Guangda Zhang
A growing number of studies have demonstrated that many complex human diseases are closely associated with microbial communities. Therefore, identifying potential microbe-disease associations is of great significance for disease diagnosis, prognosis, and treatment. However, traditional biomedical experiments are often costly, time-consuming, and labor-intensive. To address these challenges, we propose a novel computational model (HWP-SMFDCFL), for microbe-disease association prediction. Specifically, we introduce a new similarity matrix fusion algorithm (SMF) to integrate microbe and disease similarities. Second, a high-order weighted perturbation (HWP) technique is designed to dynamically assign weights to associations of different orders, thereby fully capturing high-order relational information. On this basis, a dual-path matrix factorization (DPMF) method is employed to reconstruct both original and high-order association matrices and extract low-dimensional linear features. Furthermore, by integrating hypergraph convolution and multilayer perceptron into dual-channel feature learning module (DCFL), the model captures nonlinear relationships in the microbe-disease similarity network at multiple levels, thus enhancing feature representation. Finally, Deep neural network (DNN) combined with Heterogeneous Newton boosting machine (HNBoost) is used to make the final predictions. Experimental results demonstrate that the proposed model outperforms six state-of-the-art prediction methods. Ablation Experiments and case study further validate its effectiveness and reliability. HWP-SMFDCFL is publicly available at https://github.com/senliyang/HWP-SMFDCFL.
{"title":"Inferring microbe–disease association via higher-order weighted perturbation and dual-channel feature learning based on similarity matrix fusion","authors":"Xiaoxin Du , Lisen Yang , Bo Wang , Guangda Zhang","doi":"10.1016/j.bej.2025.110015","DOIUrl":"10.1016/j.bej.2025.110015","url":null,"abstract":"<div><div>A growing number of studies have demonstrated that many complex human diseases are closely associated with microbial communities. Therefore, identifying potential microbe-disease associations is of great significance for disease diagnosis, prognosis, and treatment. However, traditional biomedical experiments are often costly, time-consuming, and labor-intensive. To address these challenges, we propose a novel computational model (HWP-SMFDCFL), for microbe-disease association prediction. Specifically, we introduce a new similarity matrix fusion algorithm (SMF) to integrate microbe and disease similarities. Second, a high-order weighted perturbation (HWP) technique is designed to dynamically assign weights to associations of different orders, thereby fully capturing high-order relational information. On this basis, a dual-path matrix factorization (DPMF) method is employed to reconstruct both original and high-order association matrices and extract low-dimensional linear features. Furthermore, by integrating hypergraph convolution and multilayer perceptron into dual-channel feature learning module (DCFL), the model captures nonlinear relationships in the microbe-disease similarity network at multiple levels, thus enhancing feature representation. Finally, Deep neural network (DNN) combined with Heterogeneous Newton boosting machine (HNBoost) is used to make the final predictions. Experimental results demonstrate that the proposed model outperforms six state-of-the-art prediction methods. Ablation Experiments and case study further validate its effectiveness and reliability. HWP-SMFDCFL is publicly available at <span><span>https://github.com/senliyang/HWP-SMFDCFL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":8766,"journal":{"name":"Biochemical Engineering Journal","volume":"227 ","pages":"Article 110015"},"PeriodicalIF":3.7,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vanillin is one of the monomers originated from lignin. We have synthesized divanillin from vanillin as a precipitate using the enzymatic reaction of horseradish peroxidase (HRP), to examine the effects of the concentration of HRP and of the reaction medium on the reactivity of the substrate and the morphology of the product, divanillin. Mathematical modeling was used to analyze the effects of solvent-induced enzyme inactivation and substrate reactivity on substrate concentration. The amount of HRP on the reaction influenced the inactivation due to the reaction media. It was quantitatively shown that changing the concentration of methanol in the reaction medium increased the solvation to the substrate vanillin and increased the contact efficiency with HRP. SEM observation of the precipitates revealed that they were spherical and needle-like and varied with the reaction conditions. Divanillin is a substance with potential for subsequent effective utilization of biomass, and this research will lead to future applications of novel materials.
{"title":"Divanillin synthesis from vanillin by horseradish peroxidase in consideration of mathematical model and morphology of product","authors":"Ikuya Teranishi, Shusuke Ito, Shintaro Morisada, Keisuke Ohto, Hidetaka Kawakita","doi":"10.1016/j.bej.2025.110016","DOIUrl":"10.1016/j.bej.2025.110016","url":null,"abstract":"<div><div>Vanillin is one of the monomers originated from lignin. We have synthesized divanillin from vanillin as a precipitate using the enzymatic reaction of horseradish peroxidase (HRP), to examine the effects of the concentration of HRP and of the reaction medium on the reactivity of the substrate and the morphology of the product, divanillin. Mathematical modeling was used to analyze the effects of solvent-induced enzyme inactivation and substrate reactivity on substrate concentration. The amount of HRP on the reaction influenced the inactivation due to the reaction media. It was quantitatively shown that changing the concentration of methanol in the reaction medium increased the solvation to the substrate vanillin and increased the contact efficiency with HRP. SEM observation of the precipitates revealed that they were spherical and needle-like and varied with the reaction conditions. Divanillin is a substance with potential for subsequent effective utilization of biomass, and this research will lead to future applications of novel materials.</div></div>","PeriodicalId":8766,"journal":{"name":"Biochemical Engineering Journal","volume":"227 ","pages":"Article 110016"},"PeriodicalIF":3.7,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145594584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L-Glutamine (L-Gln) was efficiently biosynthesized in Corynebacterium glutamicum using metabolic engineering strategies. Four C. glutamicum strains (ATCC 13032, ATCC 13809, s026, and s9114) were screened, and γ-glutamyl kinase was deleted using CRISPR/Cas12a system to redirect carbon flux, increasing L-Gln titer from 4.7 g/L to 5.3 g/L. Several glutamine synthetase (GS) were screened from different sources, and Saccharomyces cerevisiae-derived GS demonstrated to be with optimal activity. Combined with RBS optimization to enhance translational efficiency, L-Gln titer was increased to 14.62 g/L. Subsequently, a dual strategy of site-directed mutagenesis of GS and small RNA-mediated inhibition of adenylyltransferase were conducted to relieve the adenylylation of GS, increasing L-Gln titer to 19.06 g/L. A “pull-push” strategy was implemented by strengthening glutamate dehydrogenase to enhance L-glutamate precursor supply while blocking L-Gln catabolism via deletion of glutamate synthase and glutaminase, resulting in 28.51 g/L L-Gln. Self-regulatory metabolic control was achieved using a growth-responsive promoter Pcg2705 to downregulate α-ketoglutarate dehydrogenase during stationary phase, achieving a shake flask titer of 31.85 g/L. The engineered strain CG17 produced 58.96 g/L L-Gln in a 5 L fed-batch bioreactor, with a yield of 0.31 g/g glucose and productivity of 1.05 g/L/h. The work provides valuable insights for developing high-performance strains for amino acid biosynthesis.
{"title":"Rational engineering of Corynebacterium glutamicum for L-Glutamine biosynthesis","authors":"Ying-Tong Lin, Meng Chai, Qing-Hai Liu, Yu-Shu Ma, Xin-Yi Tao, Min Liu, Dong-Zhi Wei","doi":"10.1016/j.bej.2025.110011","DOIUrl":"10.1016/j.bej.2025.110011","url":null,"abstract":"<div><div><span><span>L</span></span>-Glutamine (<span>L</span>-Gln) was efficiently biosynthesized in <em>Corynebacterium glutamicum</em> using metabolic engineering strategies. Four <em>C. glutamicum</em> strains (ATCC 13032, ATCC 13809, s026, and s9114) were screened, and γ-glutamyl kinase was deleted using CRISPR/Cas12a system to redirect carbon flux, increasing <span>L</span>-Gln titer from 4.7 g/L to 5.3 g/L. Several glutamine synthetase (GS) were screened from different sources, and <em>Saccharomyces cerevisiae</em>-derived GS demonstrated to be with optimal activity. Combined with RBS optimization to enhance translational efficiency, <span>L</span>-Gln titer was increased to 14.62 g/L. Subsequently, a dual strategy of site-directed mutagenesis of GS and small RNA-mediated inhibition of adenylyltransferase were conducted to relieve the adenylylation of GS, increasing <span>L</span>-Gln titer to 19.06 g/L. A “pull-push” strategy was implemented by strengthening glutamate dehydrogenase to enhance <span>L</span>-glutamate precursor supply while blocking <span>L</span>-Gln catabolism via deletion of glutamate synthase and glutaminase, resulting in 28.51 g/L <span>L</span>-Gln. Self-regulatory metabolic control was achieved using a growth-responsive promoter Pcg2705 to downregulate α-ketoglutarate dehydrogenase during stationary phase, achieving a shake flask titer of 31.85 g/L. The engineered strain CG17 produced 58.96 g/L <span>L</span>-Gln in a 5 L fed-batch bioreactor, with a yield of 0.31 g/g glucose and productivity of 1.05 g/L/h. The work provides valuable insights for developing high-performance strains for amino acid biosynthesis.</div></div>","PeriodicalId":8766,"journal":{"name":"Biochemical Engineering Journal","volume":"227 ","pages":"Article 110011"},"PeriodicalIF":3.7,"publicationDate":"2025-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-22DOI: 10.1016/j.bej.2025.110014
Lei Huang , Zhanglong Li , Zhengtai Li , Hongjiang Li , Tiejun Bing , Yingji Li , Changyuan Yu
The blood-brain barrier (BBB) maintains brain homeostasis by selectively regulating substance exchange but also limits central nervous system (CNS) drug delivery. Blood-brain barrier penetrating peptides (BBPs) are short peptides capable of traversing the BBB, serving as drug carriers or therapeutic agents. Therefore, designing novel BBPs is crucial for overcoming the BBB's limitations and advancing CNS-targeted drug development. In this study, we propose a novel deep learning framework for BBPs design (DLF-BBP). Beginning with a Self-Attention Generative Adversarial Network (SAGAN) model to generate novel candidate BBPs that were evaluated in silico, we subsequently constructed a predictive model based on a Convolutional Neural Network (CNN) for the initial screening of GAN-designed BBPs, achieving an area under the curve (AUC) of 0.988 and an accuracy of 0.954. Further multi-step screening was conducted using physicochemical property analysis, molecular docking, and online platforms. Finally, the filtered candidate GAN-designed BBPs were validated through in vitro BBB penetration assay, leading to the identification of three promising GAN-designed BBPs with high permeation capability, demonstrating penetration comparable to that of the positive control Lixisenatide. Our research offers new possibilities for treating neurological disorders.
{"title":"DLF-BBP: A novel deep learning framework for blood-brain barrier penetrating peptides design","authors":"Lei Huang , Zhanglong Li , Zhengtai Li , Hongjiang Li , Tiejun Bing , Yingji Li , Changyuan Yu","doi":"10.1016/j.bej.2025.110014","DOIUrl":"10.1016/j.bej.2025.110014","url":null,"abstract":"<div><div>The blood-brain barrier (BBB) maintains brain homeostasis by selectively regulating substance exchange but also limits central nervous system (CNS) drug delivery. Blood-brain barrier penetrating peptides (BBPs) are short peptides capable of traversing the BBB, serving as drug carriers or therapeutic agents. Therefore, designing novel BBPs is crucial for overcoming the BBB's limitations and advancing CNS-targeted drug development. In this study, we propose a novel deep learning framework for BBPs design (DLF-BBP). Beginning with a Self-Attention Generative Adversarial Network (SAGAN) model to generate novel candidate BBPs that were evaluated in silico, we subsequently constructed a predictive model based on a Convolutional Neural Network (CNN) for the initial screening of GAN-designed BBPs, achieving an area under the curve (AUC) of 0.988 and an accuracy of 0.954. Further multi-step screening was conducted using physicochemical property analysis, molecular docking, and online platforms. Finally, the filtered candidate GAN-designed BBPs were validated through in vitro BBB penetration assay, leading to the identification of three promising GAN-designed BBPs with high permeation capability, demonstrating penetration comparable to that of the positive control Lixisenatide. Our research offers new possibilities for treating neurological disorders.</div></div>","PeriodicalId":8766,"journal":{"name":"Biochemical Engineering Journal","volume":"227 ","pages":"Article 110014"},"PeriodicalIF":3.7,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-21DOI: 10.1016/j.bej.2025.110013
Zahra Negahban , Ali Ghodba , Anne Richelle , Chris McCready , Valerie Ward , Hector Budman
In this study, we present a hybrid modeling framework that integrates piecewise Partial Least Squares (PLS) regression with Dynamic Flux Balance Analysis (dFBA) to simulate and optimize Chinese Hamster Ovary (CHO) cell fed-batch culture. Twenty-four Ambr15 experiments were conducted to systematically vary feed and inoculum compositions. Time-resolved metabolite, biomass, and Monoclonal antibodies (mAb) concentrations were collected and modeled. The hybrid model achieved high prediction accuracy (Normalized Mean Squared Error (NMSE) 0.15 for most metabolites) and provided interpretable flux profiles. Multivariate analysis revealed consistent metabolic signatures tied to media formulation, where specific feed–inoculum combinations drove shifts in glycolysis, TCA cycle flux, and nitrogen metabolism. These insights demonstrate the model’s capacity to capture key metabolic adaptations and support data-driven media optimization in CHO cell culture.
{"title":"Investigating the effect of media composition on growth and mAb production in CHO cells using a piecewise hybrid dFBA-PLS framework","authors":"Zahra Negahban , Ali Ghodba , Anne Richelle , Chris McCready , Valerie Ward , Hector Budman","doi":"10.1016/j.bej.2025.110013","DOIUrl":"10.1016/j.bej.2025.110013","url":null,"abstract":"<div><div>In this study, we present a hybrid modeling framework that integrates piecewise Partial Least Squares (PLS) regression with Dynamic Flux Balance Analysis (dFBA) to simulate and optimize Chinese Hamster Ovary (CHO) cell fed-batch culture. Twenty-four Ambr15 experiments were conducted to systematically vary feed and inoculum compositions. Time-resolved metabolite, biomass, and Monoclonal antibodies (mAb) concentrations were collected and modeled. The hybrid model achieved high prediction accuracy (Normalized Mean Squared Error (NMSE) <span><math><mo><</mo></math></span> 0.15 for most metabolites) and provided interpretable flux profiles. Multivariate analysis revealed consistent metabolic signatures tied to media formulation, where specific feed–inoculum combinations drove shifts in glycolysis, TCA cycle flux, and nitrogen metabolism. These insights demonstrate the model’s capacity to capture key metabolic adaptations and support data-driven media optimization in CHO cell culture.</div></div>","PeriodicalId":8766,"journal":{"name":"Biochemical Engineering Journal","volume":"227 ","pages":"Article 110013"},"PeriodicalIF":3.7,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145594588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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