A computational framework for proteome-wide target profiling of natural products: mechanistic and therapeutic insights into ginsenosides.

Abstract

Natural products (NPs) serve as valuable sources of pharmacologically active compounds, yet their therapeutic potential remains constrained by incomplete knowledge of their molecular targets. To address this, a computational framework based on evolutionary chemical binding similarity (ECBS) is developed to generate proteome-wide NP-target binding profiles. By integrating evolutionarily related ligands and optimizing chemical pairing strategies, the framework constructs target-specific ECBS models covering 6203 protein targets with enhanced predictive performance. As a proof of concept, this approach is applied to ginsenosides, a structurally diverse NP class with broad pharmacological relevance. The analysis reveals comprehensive ginsenoside-target associations, enabling functional clustering, inference of mechanisms of action, and identification of chemotype-specific targets. Experimental validation confirms a novel direct interaction with protein kinase C isoforms by a direct binding assay, and provides phenotypic (indirect) evidence consistent with activity at the sarcoplasmic/endoplasmic reticulum calcium adenosine triphosphatase (SERCA), demonstrating the practical utility of the approach. To enhance data accessibility and exploration, GinsenBank (http://ginseng.aslla.net) is developed as an open web resource providing predicted binding profiles, molecular clustering, multi-target analysis, and drug similarity assessment. These findings highlight the potential of proteome-scale NP-target profiling to accelerate the discovery of novel therapeutic applications for NPs.