Multifunctional molecules with both optical signal and pharmacological activity play an important role in drug development,disease diagnosis,and basic theoretical research.Aminopeptidase N(APN),as a representative tum...Multifunctional molecules with both optical signal and pharmacological activity play an important role in drug development,disease diagnosis,and basic theoretical research.Aminopeptidase N(APN),as a representative tumor biomarker with anti-tumor potential,still lacks a high-precision theranostic probe specifically targeting it.In this study,a novel quaternity design strategy for APN theranostic probe was developed.This proposed strategy utilizes advanced machine learning and molecular dynamics simulations,and cleverly employs the strategy of conformation-induced fluorescence recovery to achieve multi-objective optimization and integration of functional fragments.Through this strategy,a unique“Off-On”theranostic probe,ABTP-DPTB,was ingeniously constructed to light up APN through fluorescence restoration,relying on conformation-induced effects and solvent restriction.Differ from the common diagnostic probes,the intelligent design with non-substrated linkage makes ABTP-DPTB for long-term in-situ imaging.The fabricated probe was used for detecting and inhibiting APN in various environments,with a better in vitro inhibitory than golden-standard drug bestatin.展开更多
Monitoring the physiological changes of organelles is essential for understanding the local biological information of cells and for improving the diagnosis and therapy of diseases.Currently,fluorescent probes are cons...Monitoring the physiological changes of organelles is essential for understanding the local biological information of cells and for improving the diagnosis and therapy of diseases.Currently,fluorescent probes are considered as the most powerful tools for imaging and have been widely applied in biomedical fields.However,the expected targeting effects of these probes are often inconsistent with the real experiments.The design of fluorescent probes mainly depends on the empirical knowledge of researchers,which was inhibited by limited chemical space and low efficiency.Herein,we proposed a novel multilevel framework for the prediction of organelle-targeted fluorescent probes by employing advanced artificial intelligence algorithms.In this way,not only the targeting mechanism could be interpreted beyond intuitions but also a quick evaluation method could be established for the rational design.Furthermore,the targeting and imaging powers of the optimized and synthesized probes based on this methodology were verified by quantitative calculation and experiments.展开更多
基金supported by the National Natural Science Foundation of China(Nos.82272067,81974386,22107123,22003078,M-0696)the Science and Technology Foundation of Hunan Province(Nos.2022JJ80052,2022JJ40656)the Innovation Fund for Postgraduate Students of Central South University(Nos.2021zzts0980,2023ZZTS0842).
文摘Multifunctional molecules with both optical signal and pharmacological activity play an important role in drug development,disease diagnosis,and basic theoretical research.Aminopeptidase N(APN),as a representative tumor biomarker with anti-tumor potential,still lacks a high-precision theranostic probe specifically targeting it.In this study,a novel quaternity design strategy for APN theranostic probe was developed.This proposed strategy utilizes advanced machine learning and molecular dynamics simulations,and cleverly employs the strategy of conformation-induced fluorescence recovery to achieve multi-objective optimization and integration of functional fragments.Through this strategy,a unique“Off-On”theranostic probe,ABTP-DPTB,was ingeniously constructed to light up APN through fluorescence restoration,relying on conformation-induced effects and solvent restriction.Differ from the common diagnostic probes,the intelligent design with non-substrated linkage makes ABTP-DPTB for long-term in-situ imaging.The fabricated probe was used for detecting and inhibiting APN in various environments,with a better in vitro inhibitory than golden-standard drug bestatin.
基金the National Natural Science Foundation of China(Grant Nos.22003078,82272067,and M-0696)d the Central South University Innovation-Driven Research Program(Grant Nos.2023CXQD004 and 2023QYJC021).
文摘Monitoring the physiological changes of organelles is essential for understanding the local biological information of cells and for improving the diagnosis and therapy of diseases.Currently,fluorescent probes are considered as the most powerful tools for imaging and have been widely applied in biomedical fields.However,the expected targeting effects of these probes are often inconsistent with the real experiments.The design of fluorescent probes mainly depends on the empirical knowledge of researchers,which was inhibited by limited chemical space and low efficiency.Herein,we proposed a novel multilevel framework for the prediction of organelle-targeted fluorescent probes by employing advanced artificial intelligence algorithms.In this way,not only the targeting mechanism could be interpreted beyond intuitions but also a quick evaluation method could be established for the rational design.Furthermore,the targeting and imaging powers of the optimized and synthesized probes based on this methodology were verified by quantitative calculation and experiments.
