2D-PELSA maps multidimensional target landscapes and reveals E2-GPX4 engagement associated with ferroptosis
Year: 2026
Type: article
Abstract: Current methods for mapping ligand-protein interactions struggle to resolve weak but critical binding events and conformational dynamics, particularly for endogenous hormones with pleiotropic functions like 17β-estradiol (E2). In this study, we developed a two-dimensional (2D) PEptide-centric Local Stability Assay (PELSA). By detecting increasing ligand dose-induced local binding rates in peptide-level, 2D-PELSA allows highly reliable target identification (~ 98% true identification for staurosporine) and systematic readout of ligand-induced multidimensional structural effects (direct interaction, allosteric regulation, complex regulation, etc.), as well as direct measurement of local binding affinities in native cellular environments. We then demonstrated the versatility of 2D-PELSA by investigating the interactions of 17β-estradiol (E2), a predominant sex hormone that exerts a wide spectrum function in both health and disease. As a result, 2D-PELSA totally detected 1134 conformotypic peptides, 112 E2-responsive segments, and 37 candidate targets with weak binding affinities (> μM), which were highly enriched in the process of fatty acid beta-oxidation. Among these proteins, GPX4, a key negative regulator of ferroptosis, was found to interact with E2 with an affinity of 2.0 μM and to be degraded upon E2 treatment at micromolar concentrations. This interaction was further validated by several Western- and MS-based orthogonal methods. Subsequent phenotype characterization and omics analysis indicated that chronic exposure to E2 in the micromolar range indeed facilitated oxidative, iron-dependent ferroptosis through shaping the cellular lipid composition. As an emerging analytical platform, 2D-PELSA is poised to become an important tool for structural biology and mechanism-driven applications in toxicology and drug discovery.
Source: Nature Communications
Authors: Yanan Li, Di Yu, Haiyang Zhu, Sijie Mei, Yan Wang, Jiahua Zhou, Yuying Ye, KeJia Li, Chengfei Ruan, Weiyi Lai, Kailu Tian, Ruofan Liu, Chenyang Liu, Zhixiong Tao, Haiqian Yang, Jiahui Fang, Keyun Wang, Xiaolei Zhang, Haitao Liu, Tian Xia, Qinglong Qiao, Xinyu Liu, Guowang Xu, Mingliang Ye, Hailin Wang
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $6,990
OpenAlex ID: W7223118087
