henhaobio
Sign in

Analytical ChemistryMolecular BiologyPeer-reviewed journal article

AutoPELSA: an automated sample preparation system for proteome-wide identification of target proteins of diverse ligands

Lianji XueiD, Xi Wang, Haiqian Yang, Jie ZhouiD, Hao ZhuiD, Jinhua Shen, Weina GaoiD, Ruijun TianiD, Yan Wang, Mingliang YeiD

PDFDOIPreprint

Abstract

Protein-ligand interactions are fundamental to cellular function and drug discovery, and ligand-modification-free strategies have emerged as powerful tools for proteome-wide interrogation of these interactions. Among these, the peptide-centric local stability assay (PELSA) stands out for its high sensitivity. It uses a single digestion step to detect ligand-induced local stability shifts, enabling precise binding region localization and affinity estimation. However, manual PELSA workflows suffer from multiple labor-intensive steps that introduce variability and constrain throughput, limiting large-scale applications. To address these limitations, we developed AutoPELSA, an automated platform that streamlines the PELSA workflow including the limited proteolysis step and the following peptide separation step. AutoPELSA completes 96 samples in [~]4 h, enabling high-throughput analysis under native conditions. AutoPELSA reliably detected targets of both strong-affinity ligands (e.g., staurosporine, identifying 114 kinase targets) and low-affinity ligands, including -ketoglutarate (30 known targets). Furthermore, a mixed-ligand dose-response strategy enabled simultaneous determination of binding affinities for multiple ligand-protein interaction regions in a single experiment. Overall, AutoPELSA provides a scalable and modification-free platform for proteome-wide identification and affinity profiling of ligand-protein interactions.

Citations
1
FWCI
1.52
Field percentile
top 15%

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISZhu '25Li '25Li '23Wu '23Meng '24Pääkkönen '26
75% semantic matchBioinformatics
Haiyang Zhu, Keyun Wang, Kejia Li+4 more·Journal of Proteome Research·Aug 14, 2025· 8 citations