Machine Learning-Guided Discovery of Natural BACE1 Inhibitors with Enzymatic and Cellular Validation
TL;DR
This research provides new technology and potential lead compound for the development of future anti-Alzheimer’s disease drug as well as demonstrates that integrating machine learning-assisted virtual screening with natural product libraries is an effective strategy for discovering novel BACE1 inhibitors.
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disease characterized by excessive accumulation of β-amyloid (Aβ) peptides in the brain. Beta-secretase 1 (BACE1), the rate-limiting enzyme in the amyloidogenic processing pathway of amyloid precursor protein (APP), has been widely recognized as an important therapeutic target for reducing Aβ production. In the present study, nine machine learning-based predictive models for BACE1 inhibitors were constructed using Naïve Bayes (NB) and Recursive Partitioning (RP) algorithms. Subsequently, internal and external validations were conducted to evaluate predictive performance. The optimized models were subsequently applied to screen an in-house natural product library containing 452 compounds. Based on consensus prediction criteria, 171 compounds were identified as potential BACE1 inhibitors and subjected to fluorescence resonance energy transfer (FRET)-based enzymatic assays. Among them, 33 compounds exhibited inhibitory rates greater than 50% at an initial concentration of 50 μg/mL. Further dose–response evaluation identified five compounds with IC50 values below 50 μM, including Theaflavin-3,3′-digallate, γ-mangostin, Licoagrochalcone B, Chicoric acid, and Resveratrol. Cell-based validation using APPswe-HEK293T cells demonstrated that Theaflavin-3,3′-digallate significantly reduced extracellular Aβ1–42 secretion. Molecular docking analysis further revealed that Theaflavin-3,3′-digallate could stably bind to BACE1 through multiple hydrogen-bond and hydrophobic interactions. Collectively, this study demonstrates that integrating machine learning-assisted virtual screening with natural product libraries is an effective strategy for discovering novel BACE1 inhibitors. Theaflavin-3,3′-digallate was identified as a promising natural BACE1 inhibitor with both enzymatic and cellular activities. This research provides new technology and potential lead compound for the development of future anti-Alzheimer’s disease drug.