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
Increasing evidence showed that histone deacetylase 6(HDAC6)dysfunction is directly associated with the onset and progression of various diseases,especially cancers,making the development of HDAC6-targeted anti-tumor ...Increasing evidence showed that histone deacetylase 6(HDAC6)dysfunction is directly associated with the onset and progression of various diseases,especially cancers,making the development of HDAC6-targeted anti-tumor agents a research hotspot.In this study,artificial intelligence(AI)technology and molecular simulation strategies were fully integrated to construct an efficient and precise drug screening pipeline,which combined Voting strategy based on compound-protein interaction(CPI)prediction models,cascade molecular docking,and molecular dynamic(MD)simulations.The biological potential of the screened compounds was further evaluated through enzymatic and cellular activity assays.Among the identified compounds,Cmpd.18 exhibited more potent HDAC6 enzyme inhibitory activity(IC_(50)=5.41 nM)than that of tubastatin A(TubA)(IC_(50)=15.11 nM),along with a favorable subtype selectivity profile(selectivity index z 117.23 for HDAC1),which was further verified by the Western blot analysis.Additionally,Cmpd.18 induced G2/M phase arrest and promoted apoptosis in HCT-116 cells,exerting desirable antiproliferative activity(IC_(50)=2.59 mM).Furthermore,based on long-term MD simulation trajectory,the key residues facilitating Cmpd.18's binding were identified by decomposition free energy analysis,thereby elucidating its binding mechanism.Moreover,the representative conformation analysis also indicated that Cmpd.18 could stably bind to the active pocket in an effective conformation,thus demonstrating the potential for in-depth research of the 2-(2-phenoxyethyl)pyridazin-3(2H)-one scaffold.展开更多
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.
基金funded by Central Guidance on Local Science and Technology Development Fund of Hebei Province,China(Grant No.:226Z2605G)the Key Project from Hebei Provincial Department of Science and Technology,China(Grant No.:21372601D)+6 种基金Graduate Student Innovation Grant Program of Hebei Medical University,China(Grant No.:XCXZZB202303)Science Research Project of Hebei Education Department,China(Grant Nos.:BJ2025046,and CYZD202501)Program for Young Scientists in the Field of Natural Science of Hebei Medical University,China(Program Nos.:CYCZ2023010,CYCZ2023011,CYQD2021011,CYQD2021015 and CYQD2023012)Traditional Chinese Medicine Administration Project of Hebei Province,China(Project No.:2025427)National Natural Science Foundation of China(Grant No.:32100771)the Hebei Provincial Medical Science Research Project Plan,China(Project Nos.:20240241 and 20220200)Shijiazhuang Science and Technology Bureau,China(Grant Nos.:241200487A,and 07202204).
文摘Increasing evidence showed that histone deacetylase 6(HDAC6)dysfunction is directly associated with the onset and progression of various diseases,especially cancers,making the development of HDAC6-targeted anti-tumor agents a research hotspot.In this study,artificial intelligence(AI)technology and molecular simulation strategies were fully integrated to construct an efficient and precise drug screening pipeline,which combined Voting strategy based on compound-protein interaction(CPI)prediction models,cascade molecular docking,and molecular dynamic(MD)simulations.The biological potential of the screened compounds was further evaluated through enzymatic and cellular activity assays.Among the identified compounds,Cmpd.18 exhibited more potent HDAC6 enzyme inhibitory activity(IC_(50)=5.41 nM)than that of tubastatin A(TubA)(IC_(50)=15.11 nM),along with a favorable subtype selectivity profile(selectivity index z 117.23 for HDAC1),which was further verified by the Western blot analysis.Additionally,Cmpd.18 induced G2/M phase arrest and promoted apoptosis in HCT-116 cells,exerting desirable antiproliferative activity(IC_(50)=2.59 mM).Furthermore,based on long-term MD simulation trajectory,the key residues facilitating Cmpd.18's binding were identified by decomposition free energy analysis,thereby elucidating its binding mechanism.Moreover,the representative conformation analysis also indicated that Cmpd.18 could stably bind to the active pocket in an effective conformation,thus demonstrating the potential for in-depth research of the 2-(2-phenoxyethyl)pyridazin-3(2H)-one scaffold.
