When nanoparticles were introduced into the biological media,the protein corona would be formed,which endowed the nanoparticles with new bio-identities.Thus,controlling protein corona formation is critical to in vivo ...When nanoparticles were introduced into the biological media,the protein corona would be formed,which endowed the nanoparticles with new bio-identities.Thus,controlling protein corona formation is critical to in vivo therapeutic effect.Controlling the particle size is the most feasible method during design,and the infuence of media pH which varies with disease condition is quite important.The impact of particle size and pH on bovine serum albumin(BSA)corona formation of solid lipid nanoparticles(SLNs)was studied here.The BSA corona formation of SLNs with increasing particle size(120-480 nm)in pH 6.0 and 7.4 was investigated.Multiple techniques were employed for visualization study,conformational structure study and mechanism study,etc."BSA corona-caused aggregation"of SLN2-3 was revealed in pH 6.0 while the dispersed state of SLNs was maintained in pH 7.4,which signifcantly affected the secondary structure of BSA and cell uptake of SLNs.The main interaction was driven by van der Waals force plus hydrogen bonding in p H 7.4,while by electrostatic attraction in pH 6.0,and size-dependent adsorption was confrmed.This study provides a systematic insight to the understanding of protein corona formation of SLNs.展开更多
Compound-protein interactions(CPIs)are critical in drug discovery for identifying therapeutic targets,drug side effects,and repurposing existing drugs.Machine learning(ML)algorithms have emerged as powerful tools for ...Compound-protein interactions(CPIs)are critical in drug discovery for identifying therapeutic targets,drug side effects,and repurposing existing drugs.Machine learning(ML)algorithms have emerged as powerful tools for CPI prediction,offering notable advantages in cost-effectiveness and efficiency.This review provides an overview of recent advances in both structure-based and non-structure-based CPI prediction ML models,highlighting their performance and achievements.It also offers insights into CPI prediction-related datasets and evaluation benchmarks.Lastly,the article presents a comprehensive assessment of the current landscape of CPI prediction,elucidating the challenges faced and outlining emerging trends to advance the field.展开更多
基金the project grants from National Natural Science Foundation of China(81703431 and 81673375)the Natural Science Fund Project of Guangdong Province(2016A030312013,China)。
文摘When nanoparticles were introduced into the biological media,the protein corona would be formed,which endowed the nanoparticles with new bio-identities.Thus,controlling protein corona formation is critical to in vivo therapeutic effect.Controlling the particle size is the most feasible method during design,and the infuence of media pH which varies with disease condition is quite important.The impact of particle size and pH on bovine serum albumin(BSA)corona formation of solid lipid nanoparticles(SLNs)was studied here.The BSA corona formation of SLNs with increasing particle size(120-480 nm)in pH 6.0 and 7.4 was investigated.Multiple techniques were employed for visualization study,conformational structure study and mechanism study,etc."BSA corona-caused aggregation"of SLN2-3 was revealed in pH 6.0 while the dispersed state of SLNs was maintained in pH 7.4,which signifcantly affected the secondary structure of BSA and cell uptake of SLNs.The main interaction was driven by van der Waals force plus hydrogen bonding in p H 7.4,while by electrostatic attraction in pH 6.0,and size-dependent adsorption was confrmed.This study provides a systematic insight to the understanding of protein corona formation of SLNs.
基金supported by National Natural Science Foundation of China(T2225002,82273855 to M.Y.Z.,82204278 to X.T.L.)Lingang Laboratory(LG202102-01-02 to M.Y.Z.)+2 种基金National Key Research and Development Programof China(2022YFC3400504 toM.Y.Z.)SIMM-SHUTCM Traditional Chinese Medicine Innovation Joint Research Program(E2G805H to M.Y.Z.)Shanghai Municipal Science and TechnologyMajor Project and China Postdoctoral Science Foundation(2022M720153 to X.T.L.).
文摘Compound-protein interactions(CPIs)are critical in drug discovery for identifying therapeutic targets,drug side effects,and repurposing existing drugs.Machine learning(ML)algorithms have emerged as powerful tools for CPI prediction,offering notable advantages in cost-effectiveness and efficiency.This review provides an overview of recent advances in both structure-based and non-structure-based CPI prediction ML models,highlighting their performance and achievements.It also offers insights into CPI prediction-related datasets and evaluation benchmarks.Lastly,the article presents a comprehensive assessment of the current landscape of CPI prediction,elucidating the challenges faced and outlining emerging trends to advance the field.
