Multicomponent Mendelian randomization and machine learning studies of potential drug targets for neurodegenerative diseases

Neurodegenerative diseases (NDDs) remain a global health challenge. Alzheimer's disease (AD) and Parkinson's disease (PD) are the main types of NDDs worldwide, and Mendelian Randomization (MR) analysis across multi-omics and the entire genome offers novel strategies for identifying potential drug targets. This study used MR and summary-based MR(SMR) analysis to explore the causal relationship between genes and NDDs. Colocalization analysis and machine learning further validated and reinforced the MR findings. The pharmacological activity of candidate drug targets was confirmed via molecular docking and Molecular dynamics. This study revealed 14 genes that were closely associated with both NDDs. Specifically, IQCE(AD), HDHD2(AD), COMMD10(AD), ALPP (AD), FXYD6 (AD), STK3 (PD), LHFPL2 (PD), and ENPP4 (PD) were identified as risk factors for NDDs (OR > 1), whereas HEXIM2 (AD), TSC22D4 (AD), CHRNB1 (PD), BAG4 (PD), SLC25A1 (PD), and IL15 (PD) were protective factors (OR < 1). Molecular docking results revealed strong binding activities for PREDNISOLONE(ALPP = -7.6 kcal/mol), PANCURONIUM BROMIDE(CHRNB1 = -8 kcal/mol), CHEMBL379975(STK3 =-10.7 kcal/mol) and SIROLIMUS(IL15 = -9 kcal/mol). Molecular dynamics simulations confirmed the stable binding of the IL15-Sirolimus, ALPP-Prednisolone, STK3-CHEMBL379975, and CHRNB1-Rocuronium bromide complexes. This multi-omics study revealed 14 promising therapeutic targets for NDDs, providing new insights for targeted therapies and clinical strategies for NDDs. Our results provide evidence for future studies aimed at developing appropriate therapeutic interventions.