Among the leading methods for triggering therapeutic anti-cancer immunity is the inhibition of immune checkpoint pathways.N-glycosylation is found to be essential for the function of various immune checkpoint proteins...Among the leading methods for triggering therapeutic anti-cancer immunity is the inhibition of immune checkpoint pathways.N-glycosylation is found to be essential for the function of various immune checkpoint proteins,playing a critical role in their stability and interaction with immune cells.Removing the N-glycans of these proteins seems to be an alternative therapy,but there is a lack of a de-N-glycosylation technique for target protein specificity,which limits its clinical application.Here,we developed a novel technique for specifically removing N-glycans from a target protein on the cell surface,named deglycosylation targeting chimera(DGlyTAC),which employs a fusing protein consisting of Peptide-N-glycosidase F(PNGF)and target-specific nanobody/affibody(Nb/Af).The DGlyTAC technique was developed to target a range of glycosylated surface proteins,especially these immune checkpoints—CD24,CD47,and PD-L1,which minimally affected the overall N-glycosylation landscape and the N-glycosylation of other representative membrane proteins,ensuring high specificity and minimal off-target effects.Importantly,DGlyTAC technique was successfully applied to lead inactivation of these immune checkpoints,especially PD-L1,and showed more potential in cancer immunotherapy than inhibitors.Finally,PD-L1 targeted DGlyTAC showed therapeutic effects on several tumors in vivo,even better than PD-L1 antibody.Overall,we created a novel target-specific N-glysocylation erasing technique that establishes a modular strategy for directing membrane proteins inactivation,with broad implications on tumor immune therapeutics.展开更多
Dipeptidyl peptidase-IV(DPP-4)enzyme inhibitors are a promising category of diabetes medications.Bioactive peptides,particularly those derived from bovine milk proteins,play crucial roles in inhibiting the DPP-4 enzym...Dipeptidyl peptidase-IV(DPP-4)enzyme inhibitors are a promising category of diabetes medications.Bioactive peptides,particularly those derived from bovine milk proteins,play crucial roles in inhibiting the DPP-4 enzyme.This study describes a comprehensive strategy for DPP-4 inhibitory peptide discovery and validation that combines machine learning and virtual proteolysis techniques.Five machine learning models,including GBDT,XGBoost,LightGBM,CatBoost,and RF,were trained.Notably,LightGBM demonstrated superior performance with an AUC value of 0.92±0.01.Subsequently,LightGBM was employed to forecast the DPP-4 inhibitory potential of peptides generated through virtual proteolysis of milk proteins.Through a series of in silico screening process and in vitro experiments,GPVRGPF and HPHPHL were found to exhibit good DPP-4 inhibitory activity.Molecular docking and molecular dynamics simulations further confirmed the inhibitory mechanisms of these peptides.Through retracing the virtual proteolysis steps,it was found that GPVRGPF can be obtained fromβ-casein through enzymatic hydrolysis by chymotrypsin,while HPHPHL can be obtained fromκ-casein through enzymatic hydrolysis by stem bromelain or papain.In summary,the integration of machine learning and virtual proteolysis techniques can aid in the preliminary determination of key hydrolysis parameters and facilitate the efficient screening of bioactive peptides.展开更多
基金supported by grants from the National Key Research and Development Program of China(2022YFC3401500 and 2020YFA0803201 to P.W.)the Major Research Plan of the National Natural Science Foundation of China(Grant No.92153301 to L.Lin.)+2 种基金the National Natural Science Foundation of China(Grant No.22177126 to L.Lin.)the National Natural Science Foundation of China(82341028,31920103007 to P.W.)the Key R&D Projects in Ningxia Hui Autonomous Region(2021BFH03001).
文摘Among the leading methods for triggering therapeutic anti-cancer immunity is the inhibition of immune checkpoint pathways.N-glycosylation is found to be essential for the function of various immune checkpoint proteins,playing a critical role in their stability and interaction with immune cells.Removing the N-glycans of these proteins seems to be an alternative therapy,but there is a lack of a de-N-glycosylation technique for target protein specificity,which limits its clinical application.Here,we developed a novel technique for specifically removing N-glycans from a target protein on the cell surface,named deglycosylation targeting chimera(DGlyTAC),which employs a fusing protein consisting of Peptide-N-glycosidase F(PNGF)and target-specific nanobody/affibody(Nb/Af).The DGlyTAC technique was developed to target a range of glycosylated surface proteins,especially these immune checkpoints—CD24,CD47,and PD-L1,which minimally affected the overall N-glycosylation landscape and the N-glycosylation of other representative membrane proteins,ensuring high specificity and minimal off-target effects.Importantly,DGlyTAC technique was successfully applied to lead inactivation of these immune checkpoints,especially PD-L1,and showed more potential in cancer immunotherapy than inhibitors.Finally,PD-L1 targeted DGlyTAC showed therapeutic effects on several tumors in vivo,even better than PD-L1 antibody.Overall,we created a novel target-specific N-glysocylation erasing technique that establishes a modular strategy for directing membrane proteins inactivation,with broad implications on tumor immune therapeutics.
基金supported by the National Key Research and Development Project(2018YFE0206300-02).
文摘Dipeptidyl peptidase-IV(DPP-4)enzyme inhibitors are a promising category of diabetes medications.Bioactive peptides,particularly those derived from bovine milk proteins,play crucial roles in inhibiting the DPP-4 enzyme.This study describes a comprehensive strategy for DPP-4 inhibitory peptide discovery and validation that combines machine learning and virtual proteolysis techniques.Five machine learning models,including GBDT,XGBoost,LightGBM,CatBoost,and RF,were trained.Notably,LightGBM demonstrated superior performance with an AUC value of 0.92±0.01.Subsequently,LightGBM was employed to forecast the DPP-4 inhibitory potential of peptides generated through virtual proteolysis of milk proteins.Through a series of in silico screening process and in vitro experiments,GPVRGPF and HPHPHL were found to exhibit good DPP-4 inhibitory activity.Molecular docking and molecular dynamics simulations further confirmed the inhibitory mechanisms of these peptides.Through retracing the virtual proteolysis steps,it was found that GPVRGPF can be obtained fromβ-casein through enzymatic hydrolysis by chymotrypsin,while HPHPHL can be obtained fromκ-casein through enzymatic hydrolysis by stem bromelain or papain.In summary,the integration of machine learning and virtual proteolysis techniques can aid in the preliminary determination of key hydrolysis parameters and facilitate the efficient screening of bioactive peptides.
