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

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.