Streptomyces mobaraenesis transglutaminase(smTG)can be used for site-specific labeling of proteins with chemical groups.Here,we explored the use of modified smTG for the biosynthesis of nanobody-fluorophore conjugates...Streptomyces mobaraenesis transglutaminase(smTG)can be used for site-specific labeling of proteins with chemical groups.Here,we explored the use of modified smTG for the biosynthesis of nanobody-fluorophore conjugates(NFC).smTG catalyzes the conjugation of acyl donors containing glutamine with lysine-containing acceptors,which can lead to non-specific cross-linking.To achieve precise site-specific labeling,we employed molecular docking and virtual mutagenesis to redesign the enzyme’s substrate specificity towards the peptide GGGGQR,a non-preferred acyl donor for smTG.Starting with a thermostable and highly active smTG variant(TGm2),we identified that single mutations G250H and Y278E significantly enhanced activity against GGGGQR,increasing it by 41%and 1.13-fold,respectively.Notably,the Y278E mutation dramatically shifted the enzyme’s substrate preference,with the activity ratio against GGGGQR versus the standard substrate CBZ-Gln-Gly rising from 0.05 to 0.93.In case studies,we used nanobodies 1C12 and 7D12 as labeling targets,catalyzing their conjugation with a synthetic fluorophore via smTG variants.Nanobodies fused with GGGGQR were successfully site-specifically labeled by TGm2-Y278E,in contrast to non-specific labeling observed with other variants.These results suggest that engineering smTG for site-specific labeling is a promising approach for the biosynthesis of antibody-drug conjugates.展开更多
Food enzymes are basic components used for food processing.Through catalysis,food enzymes can function as removing allergy,enriching absorbable nutrients,improving food texture,and adjusting flavors.Food enzymes work ...Food enzymes are basic components used for food processing.Through catalysis,food enzymes can function as removing allergy,enriching absorbable nutrients,improving food texture,and adjusting flavors.Food enzymes work in various conditions,which brought out the need for engineering these enzymes with harsh environment tolerance and higher catalytic efficiency.Artificial intelligence(AI)has recently provided solutions for structural modeling,finding modification hot spots,and guiding mutations toward specific needs,which greatly benefit enzyme engineering.AI-based tools showed great advantages in cutting down the computational time,enabling higher prediction accuracy,and providing trainable models suited for wide uses.In this review,we describe the functions and uses of food enzymes,as well as their utility limitations.The necessity and advantages of using AI-based tools in enzyme engineering,and the differences between using traditional and AI-based tools are mainly discussed.Few AI-based tools for enzyme engineering were introduced and described their function.The perspective of using AI tools and the future challenges were discussed.展开更多
CO_(2)huff and puff technology can enhance the recovery of heavy oil in high-water-cut stages.However,the effectiveness of this method varies significantly under different geological and fluid conditions,which leads t...CO_(2)huff and puff technology can enhance the recovery of heavy oil in high-water-cut stages.However,the effectiveness of this method varies significantly under different geological and fluid conditions,which leads to a high-dimensional and small-sample(HDSS)dataset.It is difficult for conventional techniques that identify key factors that influence CO_(2)huff and puff effects,such as fuzzy mathematics,to manage HDSS datasets,which often contain nonlinear and irremovable abnormal data.To accurately pinpoint the primary control factors for heavy oil CO_(2)huff and puff,four machine learning classification algorithms were adopted.These algorithms were selected to align with the characteristics of HDSS datasets,taking into account algorithmic principles and an analysis of key control factors.The results demonstrated that logistic regression encounters difficulties when dealing with nonlinear data,whereas the extreme gradient boosting and gradient boosting decision tree algorithms exhibit greater sensitivity to abnormal data.By contrast,the random forest algorithm proved to be insensitive to outliers and provided a reliable ranking of factors that influence CO_(2)huff and puff effects.The top five control factors identified were the distance between parallel wells,cumulative gas injection volume,liquid production rate of parallel wells,huff and puff timing,and heterogeneous Lorentz coefficient.These research find-ings not only contribute to the precise implementation of heavy oil CO_(2)huff and puff but also offer valuable insights into selecting classification algorithms for typical HDSS data.展开更多
基金the Natural Science Foundation of Jiangsu Province(BK20202002)the Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study(Grant No.SN-ZJUSIAS-0013)+2 种基金China Postdoctoral Science Foundation(2023M741403)Jiangsu Funding Program for Excellent Postdoctoral Talent(2023ZB037)the National First-class Discipline Program of Light Industry Technology and Engineering(QGJC20230102).
文摘Streptomyces mobaraenesis transglutaminase(smTG)can be used for site-specific labeling of proteins with chemical groups.Here,we explored the use of modified smTG for the biosynthesis of nanobody-fluorophore conjugates(NFC).smTG catalyzes the conjugation of acyl donors containing glutamine with lysine-containing acceptors,which can lead to non-specific cross-linking.To achieve precise site-specific labeling,we employed molecular docking and virtual mutagenesis to redesign the enzyme’s substrate specificity towards the peptide GGGGQR,a non-preferred acyl donor for smTG.Starting with a thermostable and highly active smTG variant(TGm2),we identified that single mutations G250H and Y278E significantly enhanced activity against GGGGQR,increasing it by 41%and 1.13-fold,respectively.Notably,the Y278E mutation dramatically shifted the enzyme’s substrate preference,with the activity ratio against GGGGQR versus the standard substrate CBZ-Gln-Gly rising from 0.05 to 0.93.In case studies,we used nanobodies 1C12 and 7D12 as labeling targets,catalyzing their conjugation with a synthetic fluorophore via smTG variants.Nanobodies fused with GGGGQR were successfully site-specifically labeled by TGm2-Y278E,in contrast to non-specific labeling observed with other variants.These results suggest that engineering smTG for site-specific labeling is a promising approach for the biosynthesis of antibody-drug conjugates.
基金supported by the National Key Research and Development Program of China(No.2019YFA0706900)the National Natural Science Foundation of China(No.32071474 and 31771913).
文摘Food enzymes are basic components used for food processing.Through catalysis,food enzymes can function as removing allergy,enriching absorbable nutrients,improving food texture,and adjusting flavors.Food enzymes work in various conditions,which brought out the need for engineering these enzymes with harsh environment tolerance and higher catalytic efficiency.Artificial intelligence(AI)has recently provided solutions for structural modeling,finding modification hot spots,and guiding mutations toward specific needs,which greatly benefit enzyme engineering.AI-based tools showed great advantages in cutting down the computational time,enabling higher prediction accuracy,and providing trainable models suited for wide uses.In this review,we describe the functions and uses of food enzymes,as well as their utility limitations.The necessity and advantages of using AI-based tools in enzyme engineering,and the differences between using traditional and AI-based tools are mainly discussed.Few AI-based tools for enzyme engineering were introduced and described their function.The perspective of using AI tools and the future challenges were discussed.
基金supported by the Science Foundation of China University of Petroleum(2462019YJRC013).
文摘CO_(2)huff and puff technology can enhance the recovery of heavy oil in high-water-cut stages.However,the effectiveness of this method varies significantly under different geological and fluid conditions,which leads to a high-dimensional and small-sample(HDSS)dataset.It is difficult for conventional techniques that identify key factors that influence CO_(2)huff and puff effects,such as fuzzy mathematics,to manage HDSS datasets,which often contain nonlinear and irremovable abnormal data.To accurately pinpoint the primary control factors for heavy oil CO_(2)huff and puff,four machine learning classification algorithms were adopted.These algorithms were selected to align with the characteristics of HDSS datasets,taking into account algorithmic principles and an analysis of key control factors.The results demonstrated that logistic regression encounters difficulties when dealing with nonlinear data,whereas the extreme gradient boosting and gradient boosting decision tree algorithms exhibit greater sensitivity to abnormal data.By contrast,the random forest algorithm proved to be insensitive to outliers and provided a reliable ranking of factors that influence CO_(2)huff and puff effects.The top five control factors identified were the distance between parallel wells,cumulative gas injection volume,liquid production rate of parallel wells,huff and puff timing,and heterogeneous Lorentz coefficient.These research find-ings not only contribute to the precise implementation of heavy oil CO_(2)huff and puff but also offer valuable insights into selecting classification algorithms for typical HDSS data.
