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• Elucidation of ligand-protein interactions provides new insights into the physiological functions and mechanisms of ligand molecules, enabling new ideas for the treatment of diseases, and drug discovery and development. Most ligand-protein binding occurs only in specific regions of proteins. The identification of protein targets and binding regions is crucial for drug discovery and development, as well as for the in-depth study of drug-protein conformational relationships [1]. Current ligand modification-free methods fall into two categories: Protein-centric and peptide-centric methods [2]. The protein-centric methods, such as cellular thermal shift assay (CETSA), thermal proteome profiling (TPP), solvent induced precipitation (SIP), drug affinity responsive target stability (DARTS), and protein aggregation capture (PAC), monitor the impact of ligands on the overall thermal stability of proteins to identify targets. The peptide-centric methods, such as limited proteolysis-mass spectrometry (LiP-MS), LiP-Quant, and LiP-small molecule mapping (LiP-SMap), use limited proteolysis to generate peptides, which are then analyzed by mass spectrometry to identify target proteins and their binding regions. However, their sensitivity is limited by peptide complexity and detection throughput.

  Recently, Ye’s group reported a new strategy, namely peptide-centric local stability assay (PELSA), which enables high-sensitive and proteome-scale identification of the protein targets and binding regions of diverse ligands [3]. PELSA is based on the principle that ligand binding alters a protein’s thermodynamic stability, thereby affecting its proteolytic susceptibility. The workflow of PELSA is shown in Fig. 1a. First, cell lysates are incubated with the analyte ligand or vehicle. Second, native trypsinization is performed with high concentrations of trypsin (enzyme/substrate ratio of 1/2) for a short duration (1 min), generating a large number of peptides that represent proteins’ local stability. Ligand binding with its target proteins would inhibit the trypsin digestion on ligand-binding regions. Then, these peptides are separated from the partially digested proteins by ultrafiltration. Finally, the filtrate is collected and analyzed by liquid chromatography-tandem mass spectrometry. Peptide abundance changes between ligand-treated and control groups are compared, and the ligand-binding regions and the corresponding binding proteins are determined. Compared to existing methods, PELSA’s high enzyme usage and single-step digestion strategy significantly improve peptide coverage and detection sensitivity while avoiding interference introduced by secondary digestion.

  Figure 1

  

  Figure 1.  Establishment of PELSA. (a) Workflow of PELSA. E/S ratio (wt/wt). (b) Volcano plot visualization of all peptides from a PELSA analysis of BT474 lysates exposed to 100 nmol/L lapatinib. (c) Volcano plot as in b but on the protein level. (d) Local stability profiles to reveal ligand-binding regions. The upper and lower boundaries of the gray-shaded area represent log₂FCs of 0.3 and −0.3, respectively. The x axis represents the protein sequence from the N-terminal to the C-terminal. (e) Local affinity profiles to reveal the local binding affinity of a ligand. Heatmap representation of log₂ peptide fold changes of ERBB2 with increasing lapatinib concentrations (0, 0.1, 1, 10 and 100 µmol/L). (f) Complex structure of mTOR, rapamycin and FKBP1A (PDB 1FAP). (g) Volcano plot visualizations of all proteins from a PELSA analysis or a published LiP-MS analysis [5] of HeLa lysates exposed to 2 µmol/L rapamycin. (h) Local stability profiles of mTOR for 2 µmol/L rapamycin treatment. (b, c, g), P values, a two-sided empirical Bayes t-test (four lysate replicates), no adjustment. In all local stability plots, for peptides with |log₂FC| > 0.3, only those that passed the significance cutoff (−log₁₀P > 2, two-sided empirical Bayes t-test) were retained to ensure the reliable differences in peptide abundance. Schematics in a created using BioRender.com. Reprinted with permission [3]. Copyright 2024, Springer Nature.

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  To validate the high precision of PELSA, Ye and co-workers used lapatinib, a known inhibitor of the membrane protein tyrosine kinase receptor ERBB2, to identify its target proteins and binding regions (Figs. 1b–e). Consistent with the previous results, PELSA accurately identified the known target ERBB2 and localized its kinase domain as the binding region. Moreover, Ye and co-workers compared PELSA with the published limited proteolysis-mass spectrometry (LiP-MS) method by using rapamycin, a known drug that binds to FKBP1A. PELSA not only identified the known target protein, FKBP1A, with a 21-fold stronger fold change than LiP-MS, but also discovered interactions between other FKBP family proteins (e.g., FKBP2, 3, 4, 5, and 9) and mTOR (Figs. 1f–h). By using multiple drug doses, the binding affinities between rapamycin and FKBP1A were determined. The affinity determined by PELSA (5.6 nmol/L) is much closer to the reported value (3.8 nmol/L) than that determined by LiP-MS (>1000 nmol/L), suggesting the higher accuracy of PELSA.

  Compared to the existing methods, PELAS has several advantages. First, PELSA shows higher sensitivity and precision, and more ligand targets could be identified than those of the existing methods. PELSA is capable of identifying target proteins with stronger fold changes. The high sensitivity of PELSA makes it well-suited for detecting weak protein-ligand interactions. Second, compared to complete denaturation digestion, PELSA improves the detection efficiency of integral membrane proteins. Third, the workflow of PELSA takes only a few hours and requires no chemical labeling or complex sample preparation [4]. It is compatible with multiple quantification strategies (e.g., stable isotope dimethyl labeling), making it suitable for various applications. However, PELSA shows a bias toward integral membrane proteins, which may affect the identification of ligand interactions occurring outside the cytosolic regions of proteins. Increasing the amount of trypsin used in PELSA could be advantageous, although it would result in more complex peptide mixtures. Additionally, for proteins and regions lacking trypsin-specific cleavage sites, substituting trypsin with other proteases that target different residues could be beneficial. Despite these advantages and limitations, PELSA is expected to improve further with future advancements in mass spectrometry.

  In conclusion, this work from Ye and co-workers provides a high-sensitive and label-free strategy for proteome-wide target identification and binding-region determination. PELSA is important for drug design, protein functional studies, and the exploration of intracellular biomolecular interactions across various ligand types (metabolites, drugs, and environmental pollutants). PELSA presents a new paradigm for the detection of weak ligand-protein affinity interactions and their binding regions, providing a powerful tool for future research.

  Declaration of competing interest

  The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

  CRediT authorship contribution statement

  Jun Xiong: Writing – original draft, Formal analysis, Conceptualization. Bi-Feng Yuan: Writing – review & editing, Supervision, Investigation, Funding acquisition, Conceptualization.

  
  
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  Figure 1  Establishment of PELSA. (a) Workflow of PELSA. E/S ratio (wt/wt). (b) Volcano plot visualization of all peptides from a PELSA analysis of BT474 lysates exposed to 100 nmol/L lapatinib. (c) Volcano plot as in b but on the protein level. (d) Local stability profiles to reveal ligand-binding regions. The upper and lower boundaries of the gray-shaded area represent log₂FCs of 0.3 and −0.3, respectively. The x axis represents the protein sequence from the N-terminal to the C-terminal. (e) Local affinity profiles to reveal the local binding affinity of a ligand. Heatmap representation of log₂ peptide fold changes of ERBB2 with increasing lapatinib concentrations (0, 0.1, 1, 10 and 100 µmol/L). (f) Complex structure of mTOR, rapamycin and FKBP1A (PDB 1FAP). (g) Volcano plot visualizations of all proteins from a PELSA analysis or a published LiP-MS analysis [5] of HeLa lysates exposed to 2 µmol/L rapamycin. (h) Local stability profiles of mTOR for 2 µmol/L rapamycin treatment. (b, c, g), P values, a two-sided empirical Bayes t-test (four lysate replicates), no adjustment. In all local stability plots, for peptides with |log₂FC| > 0.3, only those that passed the significance cutoff (−log₁₀P > 2, two-sided empirical Bayes t-test) were retained to ensure the reliable differences in peptide abundance. Schematics in a created using BioRender.com. Reprinted with permission [3]. Copyright 2024, Springer Nature.

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